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Horita K, Hida K, Itatani Y, Fujita H, Hidaka Y, Yamamoto G, Ito M, Obama K. Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence. Surg Endosc 2024; 38:3461-3469. [PMID: 38760565 DOI: 10.1007/s00464-024-10874-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/20/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Most intraoperative adverse events (iAEs) result from surgeons' errors, and bleeding is the majority of iAEs. Recognizing active bleeding timely is important to ensure safe surgery, and artificial intelligence (AI) has great potential for detecting active bleeding and providing real-time surgical support. This study aimed to develop a real-time AI model to detect active intraoperative bleeding. METHODS We extracted 27 surgical videos from a nationwide multi-institutional surgical video database in Japan and divided them at the patient level into three sets: training (n = 21), validation (n = 3), and testing (n = 3). We subsequently extracted the bleeding scenes and labeled distinctively active bleeding and blood pooling frame by frame. We used pre-trained YOLOv7_6w and developed a model to learn both active bleeding and blood pooling. The Average Precision at an Intersection over Union threshold of 0.5 (AP.50) for active bleeding and frames per second (FPS) were quantified. In addition, we conducted two 5-point Likert scales (5 = Excellent, 4 = Good, 3 = Fair, 2 = Poor, and 1 = Fail) questionnaires about sensitivity (the sensitivity score) and number of overdetection areas (the overdetection score) to investigate the surgeons' assessment. RESULTS We annotated 34,117 images of 254 bleeding events. The AP.50 for active bleeding in the developed model was 0.574 and the FPS was 48.5. Twenty surgeons answered two questionnaires, indicating a sensitivity score of 4.92 and an overdetection score of 4.62 for the model. CONCLUSIONS We developed an AI model to detect active bleeding, achieving real-time processing speed. Our AI model can be used to provide real-time surgical support.
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Affiliation(s)
- Kenta Horita
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Koya Hida
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Yoshiro Itatani
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Haruku Fujita
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yu Hidaka
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Goshiro Yamamoto
- Division of Medical Information Technology and Administration Planning, Kyoto University, Kyoto, Japan
| | - Masaaki Ito
- Surgical Device Innovation Office, National Cancer Center Hospital East, Chiba, Japan
- Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan
| | - Kazutaka Obama
- Department of Surgery, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
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Okumura T, Imai K, Misawa M, Kudo SE, Hotta K, Ito S, Kishida Y, Takada K, Kawata N, Maeda Y, Yoshida M, Yamamoto Y, Minamide T, Ishiwatari H, Sato J, Matsubayashi H, Ono H. Evaluating false-positive detection in a computer-aided detection system for colonoscopy. J Gastroenterol Hepatol 2024; 39:927-934. [PMID: 38273460 DOI: 10.1111/jgh.16491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIM Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP. METHODS We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching. RESULTS During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87). CONCLUSIONS The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.
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Affiliation(s)
- Taishi Okumura
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kenichiro Imai
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Sayo Ito
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Kazunori Takada
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Noboru Kawata
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yuki Maeda
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masao Yoshida
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yoichi Yamamoto
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Junya Sato
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Hiroyuki Ono
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
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Guo F, Meng H. Application of artificial intelligence in gastrointestinal endoscopy. Arab J Gastroenterol 2024; 25:93-96. [PMID: 38228443 DOI: 10.1016/j.ajg.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/06/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.
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Affiliation(s)
- Fujia Guo
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China
| | - Hua Meng
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China.
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4
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Abdelrahim M, Siggens K, Iwadate Y, Maeda N, Htet H, Bhandari P. New AI model for neoplasia detection and characterisation in inflammatory bowel disease. Gut 2024; 73:725-728. [PMID: 38395438 DOI: 10.1136/gutjnl-2023-330718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/20/2023] [Indexed: 02/25/2024]
Affiliation(s)
- Mohamed Abdelrahim
- Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
- Royal Devon and Exeter Hospital, Exeter, UK
| | - Katie Siggens
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | | | | | - Hein Htet
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
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Takahashi H, Ohno E, Furukawa T, Yamao K, Ishikawa T, Mizutani Y, Iida T, Shiratori Y, Oyama S, Koyama J, Mori K, Hayashi Y, Oda M, Suzuki T, Kawashima H. Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis. Dig Endosc 2024; 36:463-472. [PMID: 37448120 DOI: 10.1111/den.14622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
OBJECTIVES In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.
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Affiliation(s)
- Hidekazu Takahashi
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Eizaburo Ohno
- Department of Gastroenterology and Hepatology, Fujita Health University Graduate School of Medicine, Aichi, Japan
| | - Taiki Furukawa
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Kentaro Yamao
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | | | - Shintaro Oyama
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Junji Koyama
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Kensaku Mori
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Yuichiro Hayashi
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Masahiro Oda
- Information Strategy Office, Information and Communications, Nagoya University, Aichi, Japan
| | - Takahisa Suzuki
- Department of Gastroenterology, Toyota Memorial Hospital, Aichi, Japan
| | - Hiroki Kawashima
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
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Nakamura H, Fukuda M, Matsuda A, Makino N, Kimura H, Ohtaki Y, Nawa Y, Oyama S, Suzuki Y, Kobayashi T, Ishizawa T, Kakizaki Y, Ueno Y. Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning. DEN OPEN 2024; 4:e344. [PMID: 38434146 PMCID: PMC10908399 DOI: 10.1002/deo2.344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 03/05/2024]
Abstract
Objectives Localized autoimmune pancreatitis is difficult to differentiate from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. In recent years, deep learning methods have improved the diagnosis of diseases. Hence, we developed a special cross-validation framework to search for effective methodologies of deep learning in distinguishing autoimmune pancreatitis from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. Methods Data from 24 patients diagnosed with localized autoimmune pancreatitis (8751 images) and 61 patients diagnosed with pancreatic ductal adenocarcinoma (20,584 images) were collected from 2016 to 2022. We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. We divided patients into five groups according to different factors for 5-fold cross-validation, where the ordered and balanced datasets were created for the performance evaluations. Results ResNet152 surpassed the endoscopists in all evaluation metrics with almost all datasets. Interestingly, when the dataset is balanced according to the factor of the endoscopists' diagnostic accuracy, the area under the receiver operating characteristic curve and accuracy were highest at 0.85 and 0.80, respectively. Conclusions It is deduced that image features useful for ResNet152 correlate with those used by endoscopists for their diagnoses. This finding may contribute to sample-efficient dataset preparation to train convolutional neural networks for endoscopic ultrasonography-imaging diagnosis.
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Affiliation(s)
- Hitomi Nakamura
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Motohisa Fukuda
- Department of ScienceFaculty of ScienceYamagata UniversityYamagataJapan
| | - Akiko Matsuda
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Naohiko Makino
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | | | - Yu Ohtaki
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Yoshihito Nawa
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Soushi Oyama
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Yuya Suzuki
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Toshikazu Kobayashi
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Tetsuya Ishizawa
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Yasuharu Kakizaki
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
| | - Yoshiyuki Ueno
- Department of GastroenterologyFaculty of MedicineYamagata UniversityYamagataJapan
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024:S1590-8658(24)00249-4. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Tiankanon K, Karuehardsuwan J, Aniwan S, Mekaroonkamol P, Sunthornwechapong P, Navadurong H, Tantitanawat K, Mekritthikrai K, Samutrangsi S, Vateekul P, Rerknimitr R. Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand. Clin Endosc 2024; 57:217-225. [PMID: 38556473 PMCID: PMC10984740 DOI: 10.5946/ce.2023.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 09/25/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND/AIMS This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. METHODS Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. RESULTS In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. CONCLUSION Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.
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Affiliation(s)
- Kasenee Tiankanon
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Julalak Karuehardsuwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Satimai Aniwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Parit Mekaroonkamol
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | | | - Huttakan Navadurong
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Kittithat Tantitanawat
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Krittaya Mekritthikrai
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Salin Samutrangsi
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
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Yu P, Fang C, Liu X, Fu W, Ling J, Yan Z, Jiang Y, Cao Z, Wu M, Chen Z, Zhu W, Zhang Y, Abudukeremu A, Wang Y, Liu X, Wang J. Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study. JMIR MEDICAL EDUCATION 2024; 10:e48514. [PMID: 38335017 PMCID: PMC10891494 DOI: 10.2196/48514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/04/2023] [Accepted: 12/11/2023] [Indexed: 02/10/2024]
Abstract
BACKGROUND ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. OBJECTIVE The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. METHODS A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. RESULTS Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. CONCLUSIONS Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.
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Affiliation(s)
- Peng Yu
- Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Changchang Fang
- Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Xiaolin Liu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Wanying Fu
- Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Jitao Ling
- Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Zhiwei Yan
- College of Kinesiology, Shenyang Sport University, Shenyang, China
| | - Yuan Jiang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhengyu Cao
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Maoxiong Wu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhiteng Chen
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wengen Zhu
- Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuling Zhang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ayiguli Abudukeremu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yue Wang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiao Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jingfeng Wang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
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Chino A, Ide D, Abe S, Yoshinaga S, Ichimasa K, Kudo T, Ninomiya Y, Oka S, Tanaka S, Igarashi M. Performance evaluation of a computer-aided polyp detection system with artificial intelligence for colonoscopy. Dig Endosc 2024; 36:185-194. [PMID: 37099623 DOI: 10.1111/den.14578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/25/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVES A computer-aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand-alone performance of this device under blinded conditions. METHODS This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. RESULTS Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8-98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3-95.8%). For the frame-based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8-88.4%), 84.7% (95% CI 83.8-85.6%), 34.9% (95% CI 32.3-37.4%), and 98.2% (95% CI 97.8-98.5%), respectively. TRIAL REGISTRATION University Hospital Medical Information Network (UMIN000044622).
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Affiliation(s)
- Akiko Chino
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Daisuke Ide
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Tokyo Endoscopic Clinic, Tokyo, Japan
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuki Ninomiya
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Masahiro Igarashi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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12
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Zhang H, Wu Q, Sun J, Wang J, Zhou L, Cai W, Zou D. A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial. Front Med (Lausanne) 2024; 10:1341259. [PMID: 38327275 PMCID: PMC10847558 DOI: 10.3389/fmed.2023.1341259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists' performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting. Methods The CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18-75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists. Results A total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p < 0.001), and the specificity of the endoscopists in these two groups was comparable (100.00 vs. 100.00%). In a subgroup analysis, the CADe system improved the PDR of the 6-9 mm polyps (18.04 vs. 13.85%, p < 0.05) and reduced the miss detection rate, especially at 10:00-12:00 am (12.5 vs. 39.81%, p < 0.001). Conclusion The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC. Registration This clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988). Clinical trial registration website www.chictr.org.cn, identifier ChiCTR2100041988.
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Affiliation(s)
- Heng Zhang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wu
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing Sun
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wang
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Zhou
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Cai
- Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Nduma BN, Nkeonye S, Uwawah TD, Kaur D, Ekhator C, Ambe S. Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer. Cureus 2024; 16:e53024. [PMID: 38410294 PMCID: PMC10895204 DOI: 10.7759/cureus.53024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 02/28/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.
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Affiliation(s)
| | - Stephen Nkeonye
- Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Davinder Kaur
- Internal Medicine, Medical City, North Richland Hills, USA
| | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, USA
| | - Solomon Ambe
- Neurology, Baylor Scott & White Health, McKinney, USA
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Zhang H, Yang X, Tao Y, Zhang X, Huang X. Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis. PLoS One 2023; 18:e0294930. [PMID: 38113199 PMCID: PMC10729963 DOI: 10.1371/journal.pone.0294930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees. METHODS EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach. RESULTS A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias. CONCLUSIONS The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.
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Affiliation(s)
- Hangbin Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyu Yang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ye Tao
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyi Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuan Huang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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15
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Bordbar M, Helfroush MS, Danyali H, Ejtehadi F. Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model. Biomed Eng Online 2023; 22:124. [PMID: 38098015 PMCID: PMC10722702 DOI: 10.1186/s12938-023-01186-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames. METHODS In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy. RESULTS 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study. CONCLUSION The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.
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Affiliation(s)
- Mehrdokht Bordbar
- Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
| | | | - Habibollah Danyali
- Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Fardad Ejtehadi
- Department of Internal Medicine, Gastroenterohepatology Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
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Gan T, Jin Z, Yu L, Liang X, Zhang H, Ye X. Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection. Sci Rep 2023; 13:21655. [PMID: 38066207 PMCID: PMC10709402 DOI: 10.1038/s41598-023-49057-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.
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Affiliation(s)
- Tianyuan Gan
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Ziyi Jin
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Liangliang Yu
- Department of Gastroenterology, Endoscopy Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | - Xiao Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | - Hong Zhang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Xuesong Ye
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
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17
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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18
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Fang C, Wu Y, Fu W, Ling J, Wang Y, Liu X, Jiang Y, Wu Y, Chen Y, Zhou J, Zhu Z, Yan Z, Yu P, Liu X. How does ChatGPT-4 preform on non-English national medical licensing examination? An evaluation in Chinese language. PLOS DIGITAL HEALTH 2023; 2:e0000397. [PMID: 38039286 PMCID: PMC10691691 DOI: 10.1371/journal.pdig.0000397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/23/2023] [Indexed: 12/03/2023]
Abstract
ChatGPT, an artificial intelligence (AI) system powered by large-scale language models, has garnered significant interest in healthcare. Its performance dependent on the quality and quantity of training data available for a specific language, with the majority of it being in English. Therefore, its effectiveness in processing the Chinese language, which has fewer data available, warrants further investigation. This study aims to assess the of ChatGPT's ability in medical education and clinical decision-making within the Chinese context. We utilized a dataset from the Chinese National Medical Licensing Examination (NMLE) to assess ChatGPT-4's proficiency in medical knowledge in Chinese. Performance indicators, including score, accuracy, and concordance (confirmation of answers through explanation), were employed to evaluate ChatGPT's effectiveness in both original and encoded medical questions. Additionally, we translated the original Chinese questions into English to explore potential avenues for improvement. ChatGPT scored 442/600 for original questions in Chinese, surpassing the passing threshold of 360/600. However, ChatGPT demonstrated reduced accuracy in addressing open-ended questions, with an overall accuracy rate of 47.7%. Despite this, ChatGPT displayed commendable consistency, achieving a 75% concordance rate across all case analysis questions. Moreover, translating Chinese case analysis questions into English yielded only marginal improvements in ChatGPT's performance (p = 0.728). ChatGPT exhibits remarkable precision and reliability when handling the NMLE in Chinese. Translation of NMLE questions from Chinese to English does not yield an improvement in ChatGPT's performance.
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Affiliation(s)
- Changchang Fang
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
- Queen Mary College, Nanchang University, Jiangxi, China
| | - Yuting Wu
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Wanying Fu
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
- Queen Mary College, Nanchang University, Jiangxi, China
| | - Jitao Ling
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yue Wang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Xiaolin Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Yuan Jiang
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Yifan Wu
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yixuan Chen
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Jing Zhou
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Zhichen Zhu
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Zhiwei Yan
- Provincial University Key Laboratory of Sport and Health Science, School of Physical Education and Sport Sciences, Fujian Normal University, Fuzhou, China
| | - Peng Yu
- Department of Endocrine, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
- Institute for the Study of Endocrinology and Metabolism in Jiangxi, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Xiao Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
- Institute for the Study of Endocrinology and Metabolism in Jiangxi, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
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Sumioka A, Tsuboi A, Oka S, Kato Y, Matsubara Y, Hirata I, Takigawa H, Yuge R, Shimamoto F, Tada T, Tanaka S. Disease surveillance evaluation of primary small-bowel follicular lymphoma using capsule endoscopy images based on a deep convolutional neural network (with video). Gastrointest Endosc 2023; 98:968-976.e3. [PMID: 37482106 DOI: 10.1016/j.gie.2023.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/01/2023] [Accepted: 07/09/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND AND AIMS Capsule endoscopy (CE) is useful in evaluating disease surveillance for primary small-bowel follicular lymphoma (FL), but some cases are difficult to evaluate objectively. This study evaluated the usefulness of a deep convolutional neural network (CNN) system using CE images for disease surveillance of primary small-bowel FL. METHODS We enrolled 26 consecutive patients with primary small-bowel FL diagnosed between January 2011 and January 2021 who underwent CE before and after a watch-and-wait strategy or chemotherapy. Disease surveillance by the CNN system was evaluated by the percentage of FL-detected images among all CE images of the small-bowel mucosa. RESULTS Eighteen cases (69%) were managed with a watch-and-wait approach, and 8 cases (31%) were treated with chemotherapy. Among the 18 cases managed with the watch-and-wait approach, the outcome of lesion evaluation by the CNN system was almost the same in 13 cases (72%), aggravation in 4 (22%), and improvement in 1 (6%). Among the 8 cases treated with chemotherapy, the outcome of lesion evaluation by the CNN system was improvement in 5 cases (63%), almost the same in 2 (25%), and aggravation in 1 (12%). The physician and CNN system reported similar results regarding disease surveillance evaluation in 23 of 26 cases (88%), whereas a discrepancy between the 2 was found in the remaining 3 cases (12%), attributed to poor small-bowel cleansing level. CONCLUSIONS Disease surveillance evaluation of primary small-bowel FL using CE images by the developed CNN system was useful under the condition of excellent small-bowel cleansing level.
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Affiliation(s)
- Akihiko Sumioka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Akiyoshi Tsuboi
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | | | - Yuka Matsubara
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Issei Hirata
- Department of Gastroenterology, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidehiko Takigawa
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Ryo Yuge
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Fumio Shimamoto
- Faculty of Health Sciences, Hiroshima Shudo University, Hiroshima, Japan
| | - Tomohiro Tada
- AI Medical Service Inc, Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
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20
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Hsu WF, Chiu HM. Optimization of colonoscopy quality: Comprehensive review of the literature and future perspectives. Dig Endosc 2023; 35:822-834. [PMID: 37381701 DOI: 10.1111/den.14627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 06/27/2023] [Indexed: 06/30/2023]
Abstract
Colonoscopy is crucial in preventing colorectal cancer (CRC) and reducing associated mortality. This comprehensive review examines the importance of high-quality colonoscopy and associated quality indicators, including bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate (ADR), complete resection, specimen retrieval, complication rates, and patient satisfaction, while also discussing other ADR-related metrics. Additionally, the review draws attention to often overlooked quality aspects, such as nonpolypoid lesion detection, as well as insertion and withdrawal skills. Moreover, it explores the potential of artificial intelligence in enhancing colonoscopy quality and highlights specific considerations for organized screening programs. The review also emphasizes the implications of organized screening programs and the need for continuous quality improvement. A high-quality colonoscopy is crucial for preventing postcolonoscopy CRC- and CRC-related deaths. Health-care professionals must develop a thorough understanding of colonoscopy quality components, including technical quality, patient safety, and patient experience. By prioritizing ongoing evaluation and refinement of these quality indicators, health-care providers can contribute to improved patient outcomes and develop more effective CRC screening programs.
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Affiliation(s)
- Wen-Feng Hsu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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21
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Ding M, Yan J, Chao G, Zhang S. Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review). Oncol Rep 2023; 50:199. [PMID: 37772392 DOI: 10.3892/or.2023.8636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/07/2023] [Indexed: 09/30/2023] Open
Abstract
Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.
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Affiliation(s)
- Menglu Ding
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Junbin Yan
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Guanqun Chao
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
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22
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Choksi S, Szot S, Zang C, Yarali K, Cao Y, Ahmad F, Xiang Z, Bitner DP, Kostic Z, Filicori F. Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition. Surg Endosc 2023; 37:8778-8784. [PMID: 37580578 DOI: 10.1007/s00464-023-10322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/19/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Automation of surgical phase recognition is a key effort toward the development of Computer Vision (CV) algorithms, for workflow optimization and video-based assessment. CV is a form of Artificial Intelligence (AI) that allows interpretation of images through a deep learning (DL)-based algorithm. The improvements in Graphic Processing Unit (GPU) computing devices allow researchers to apply these algorithms for recognition of content in videos in real-time. Edge computing, where data is collected, analyzed, and acted upon in close proximity to the collection source, is essential meet the demands of workflow optimization by providing real-time algorithm application. We implemented a real-time phase recognition workflow and demonstrated its performance on 10 Robotic Inguinal Hernia Repairs (RIHR) to obtain phase predictions during the procedure. METHODS Our phase recognition algorithm was developed with 211 videos of RIHR originally annotated into 14 surgical phases. Using these videos, a DL model with a ResNet-50 backbone was trained and validated to automatically recognize surgical phases. The model was deployed to a GPU, the Nvidia® Jetson Xavier™ NX edge computing device. RESULTS This model was tested on 10 inguinal hernia repairs from four surgeons in real-time. The model was improved using post-recording processing methods such as phase merging into seven final phases (peritoneal scoring, mesh placement, preperitoneal dissection, reduction of hernia, out of body, peritoneal closure, and transitionary idle) and averaging of frames. Predictions were made once per second with a processing latency of approximately 250 ms. The accuracy of the real-time predictions ranged from 59.8 to 78.2% with an average accuracy of 68.7%. CONCLUSION A real-time phase prediction of RIHR using a CV deep learning model was successfully implemented. This real-time CV phase segmentation system can be useful for monitoring surgical progress and be integrated into software to provide hospital workflow optimization.
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Affiliation(s)
- Sarah Choksi
- Intraoperative Performance Analytics Laboratory (IPAL), Department of Surgery, Lenox Hill Hospital, Northwell Health, 186 E 76th Street, 1st Fl, New York, NY, 10021, USA.
| | - Skyler Szot
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Chengbo Zang
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Kaan Yarali
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Yuqing Cao
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Feroz Ahmad
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Zixuan Xiang
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Daniel P Bitner
- Intraoperative Performance Analytics Laboratory (IPAL), Department of Surgery, Lenox Hill Hospital, Northwell Health, 186 E 76th Street, 1st Fl, New York, NY, 10021, USA
| | - Zoran Kostic
- Department of Electrical Engineering, Columbia University, 500 W 120 Street, Mudd 1310, New York, NY, 10027, USA
| | - Filippo Filicori
- Intraoperative Performance Analytics Laboratory (IPAL), Department of Surgery, Lenox Hill Hospital, Northwell Health, 186 E 76th Street, 1st Fl, New York, NY, 10021, USA
- Zucker School of Medicine at Hofstra/Northwell Health, 5000 Hofstra Blvd, Hempstead, NY, 11549, USA
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23
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Leśniewska M, Patryn R, Kopystecka A, Kozioł I, Budzyńska J. Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms. J Clin Med 2023; 12:6721. [PMID: 37959187 PMCID: PMC10650785 DOI: 10.3390/jcm12216721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Gastrointestinal cancers are characterized by high incidence and mortality. However, there are well-established methods of screening. The endoscopy exam provides the macroscopical image and enables harvesting the tissue samples for further histopathological diagnosis. The efficiency of endoscopies relies not only on proper patient preparation, but also on the skills of the personnel conducting the exam. In recent years, a number of reports concerning the application of artificial intelligence (AI) in medicine have arisen. Numerous studies aimed to assess the utility of deep learning/ neural network systems supporting endoscopies. In this review, we summarized the most recent reports and randomized clinical trials regarding the application of AI in screening and surveillance of gastrointestinal cancers among patients suffering from esophageal, gastric, and colorectal cancer, along with the advantages, limitations, and controversies of those novel solutions.
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Affiliation(s)
- Magdalena Leśniewska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Rafał Patryn
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
| | - Agnieszka Kopystecka
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Ilona Kozioł
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
| | - Julia Budzyńska
- Students’ Scientific Circle on Medical Law at the Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland; (M.L.); (A.K.); (I.K.); (J.B.)
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24
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Abraham A, Jose R, Ahmad J, Joshi J, Jacob T, Khalid AUR, Ali H, Patel P, Singh J, Toma M. Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps. J Imaging 2023; 9:215. [PMID: 37888322 PMCID: PMC10607441 DOI: 10.3390/jimaging9100215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/29/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.
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Affiliation(s)
- Adriel Abraham
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Rejath Jose
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Jawad Ahmad
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Jai Joshi
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Thomas Jacob
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Aziz-ur-rahman Khalid
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Hassam Ali
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC 27858, USA;
| | - Pratik Patel
- Department of Gastroenterology, Northwell Mather Hospital, Port Jefferson, NY 11777, USA (J.S.)
| | - Jaspreet Singh
- Department of Gastroenterology, Northwell Mather Hospital, Port Jefferson, NY 11777, USA (J.S.)
| | - Milan Toma
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
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25
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Mizukami K, Fushimi E, Sagami R, Abe T, Sato T, Terashi S, Fukuda M, Nishikiori H, Nagai T, Kodama M, Murakami K. Usefulness of AI-Equipped Endoscopy for Detecting Colorectal Adenoma during Colonoscopy Screening: Confirm That Colon Neoplasm Finely Can Be Identified by AI without Overlooking Study (Confidential Study). J Clin Med 2023; 12:6332. [PMID: 37834976 PMCID: PMC10573595 DOI: 10.3390/jcm12196332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/28/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
In the present prospective case series study, we investigated the lesion-detection ability of an AI-equipped colonoscopy as an addition to colonoscopy (CS) screening. Participants were 100 patients aged ≥20 years who had not undergone CS at the study site in the last 3 years and passed the exclusion criteria. CS procedures were conducted using conventional white light imaging and computer-aided detection (CADe). Adenoma detection rate (ADR; number of individuals with at least one adenoma detected) was compared between the conventional group and the CADe group. Of the 170 lesions identified, the ADR of the CADe group was significantly higher than the ADR of the conventional group (69% vs. 61%, p = 0.008). For the expert endoscopists, although ADR did not differ significantly, the mean number of detected adenomas per procedure (MAP) was significantly higher in the CADe group than in the conventional group (1.7 vs. 1.45, p = 0.034). For non-expert endoscopists, ADR and MAP were significantly higher in the CADe group than in the conventional group (ADR 69.5% vs. 56.6%, p = 0.016; MAP 1.66 vs. 1.11, p < 0.001). These results indicate that the CADe function in CS screening has a positive effect on adenoma detection, especially for non-experts.
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Affiliation(s)
- Kazuhiro Mizukami
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Erina Fushimi
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Ryota Sagami
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Takashi Abe
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Takao Sato
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Shohei Terashi
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Masahide Fukuda
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Hidefumi Nishikiori
- Department of Gastroenterology, Oita San-ai Medical Center, 1213 Ichi, Oita 870-1151, Japan
| | - Takayuki Nagai
- Department of Gastroenterology, Oita Koseiren Tsurumi Hospital, 4333, Tsurumi, Beppu, Oita 874-8585, Japan
| | - Masaaki Kodama
- Department of Advanced Medical Sciences, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Faculty of Medicine, Oita University, 1-1, Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
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26
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Sengun B, Iscan Y, Tataroglu Ozbulak GA, Kumbasar N, Egriboz E, Sormaz IC, Aksakal N, Deniz SM, Haklidir M, Tunca F, Giles Senyurek Y. Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein. Surg Laparosc Endosc Percutan Tech 2023; 33:327-331. [PMID: 37311027 DOI: 10.1097/sle.0000000000001185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 04/18/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and deep learning algorithms to identify anatomic structures during laparoscopic and robot-assisted surgery can be used to provide real-time guidance. METHODS In this experimental feasibility study, intraoperative videos of patients who underwent minimally invasive transabdominal left adrenalectomy procedures between 2011 and 2022 in a tertiary endocrine referral center were retrospectively analyzed and used to develop an artificial intelligence model. Semantic segmentation of the left adrenal vein with deep learning was performed. To train a model, 50 random images per patient were captured during the identification and dissection of the left adrenal vein. A randomly selected 70% of data was used to train models while 15% for testing and 15% for validation with 3 efficient stage-wise feature pyramid networks (ESFPNet). Dice similarity coefficient (DSC) and intersection over union scores were used to evaluate segmentation accuracy. RESULTS A total of 40 videos were analyzed. Annotation of the left adrenal vein was performed in 2000 images. The segmentation network training on 1400 images was used to identify the left adrenal vein in 300 test images. The mean DSC and sensitivity for the highest scoring efficient stage-wise feature pyramid network B-2 network were 0.77 (±0.16 SD) and 0.82 (±0.15 SD), respectively, while the maximum DSC was 0.93, suggesting a successful prediction of anatomy. CONCLUSIONS Deep learning algorithms can predict the left adrenal vein anatomy with high performance and can potentially be utilized to identify critical anatomy during adrenal surgery and provide real-time guidance in the near future.
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Affiliation(s)
- Berke Sengun
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Yalin Iscan
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | | | | | | | - Ismail C Sormaz
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Nihat Aksakal
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | | | | | - Fatih Tunca
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Yasemin Giles Senyurek
- Department of General Surgery, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
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27
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Zhang Z, Chen X, Cao T, Ning Y, Wang H, Wang F, Zhao Q, Fang J. Polyps are detected more often in early colonoscopies. Scand J Gastroenterol 2023; 58:1085-1090. [PMID: 37122125 DOI: 10.1080/00365521.2023.2202293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE To examine the time variation in polyp detection for colonoscopies performed in a tertiary hospital and to explore independent factors that predict polyp detection rate (PDR). METHODS Data on all patients who underwent colonoscopy for the diagnostic purpose at our endoscopy center in Zhongnan Hospital of Wuhan University from January 2021 to December 2021 were reviewed. The start time of included colonoscopies for eligible patients was recorded. PDR and polyps detected per colonoscopy (PPC) were calculated. The endoscopists' schedules were classified into full-day and half-day shifts according to their participation in the morning and afternoon colonoscopies. RESULTS Data on a total of 12116 colonoscopies were analyzed, with a PDR of 38.03% for all the patients and 46.38% for patients ≥50 years. PDR and PPC significantly decreased as the day progressed (both p < .001). For patients ≥50 years, PDR declined below 40% at 13:00-13:59 and 16:00-16:59. The PDR in the morning was higher than that in the afternoon for both half-day (p = .019) and full-day procedures (p < .001). In multivariate analysis, start time, patient gender, age, conscious sedation, and bowel preparation quality significantly predicted PDR (p < .001). CONCLUSIONS The polyp detection declined as the day progressed. A continuous work schedule resulted in a subpar PDR. Colonoscopies performed in the morning had a higher PDR than that in the afternoon. Patient gender, age, conscious sedation, and bowel preparation quality were identified as the independent predictors of PDR.
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Affiliation(s)
- Zhang Zhang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaojia Chen
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Tingting Cao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yumei Ning
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haizhou Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Fan Wang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Qiu Zhao
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jun Fang
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China
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28
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Maida M, Marasco G, Facciorusso A, Shahini E, Sinagra E, Pallio S, Ramai D, Murino A. Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now. Expert Rev Anticancer Ther 2023; 23:719-729. [PMID: 37194308 DOI: 10.1080/14737140.2023.2215436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy. AREAS COVERED This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives. EXPERT OPINION Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.
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Affiliation(s)
- Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy
| | - Giovanni Marasco
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS "Saverio de Bellis", Castellana Grotte, Bari, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalu, Italy
| | - Socrate Pallio
- Digestive Diseases Endoscopy Unit, Policlinico G. Martino Hospital, University of Messina, Messina, Italy
| | - Daryl Ramai
- Gastroenterology & Hepatology, University of Utah Health, Salt Lake City, UT, USA
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, UK
- Department of Gastroenterology, Cleveland Clinic London, London, UK
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Spadaccini M, Schilirò A, Sharma P, Repici A, Hassan C, Voza A. Adenoma detection rate in colonoscopy: how can it be improved? Expert Rev Gastroenterol Hepatol 2023; 17:1089-1099. [PMID: 37869781 DOI: 10.1080/17474124.2023.2273990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 10/18/2023] [Indexed: 10/24/2023]
Abstract
INTRODUCTION The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. AREAS COVERED In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR. EXPERT OPINION Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Schilirò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Antonio Voza
- Humanitas Clinical and Research Center -IRCCS-, Emergency Department, Rozzano, Italy
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Sharma A, Kumar R, Yadav G, Garg P. Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Lett 2023; 565:216238. [PMID: 37211068 DOI: 10.1016/j.canlet.2023.216238] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India; Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India.
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Shimizu T, Sasaki Y, Ito K, Matsuzaka M, Sakuraba H, Fukuda S. A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging. Sci Rep 2023; 13:7510. [PMID: 37161081 PMCID: PMC10169849 DOI: 10.1038/s41598-023-34750-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/06/2023] [Indexed: 05/11/2023] Open
Abstract
Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1,390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 598,801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs.
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Affiliation(s)
- Takeshi Shimizu
- Department of Gastroenterology, Sendai City Medical Center Sendai Open Hospital, 5-22-1 Tsurugaya, Miyagino-ku, Sendai, 983-0824, Japan
| | - Yoshihiro Sasaki
- Department of Medical Informatics, Hirosaki University Hospital, 53 Hon-cho, Hirosaki, 036-8563, Japan.
| | - Kei Ito
- Department of Gastroenterology, Sendai City Medical Center Sendai Open Hospital, 5-22-1 Tsurugaya, Miyagino-ku, Sendai, 983-0824, Japan
| | - Masashi Matsuzaka
- Department of Medical Informatics, Hirosaki University Hospital, 53 Hon-cho, Hirosaki, 036-8563, Japan
| | - Hirotake Sakuraba
- Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, 036-8562, Japan
| | - Shinsaku Fukuda
- Department of Community Medical Support, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, 036-8562, Japan
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Mazumdar S, Sinha S, Jha S, Jagtap B. Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system; first indigenous algorithm developed in India. Indian J Gastroenterol 2023; 42:226-232. [PMID: 37145230 DOI: 10.1007/s12664-022-01331-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 12/18/2022] [Indexed: 05/06/2023]
Abstract
BACKGROUND Colonic polyps can be detected and resected during a colonoscopy before cancer development. However, about 1/4th of the polyps could be missed due to their small size, location or human errors. An artificial intelligence (AI) system can improve polyp detection and reduce colorectal cancer incidence. We are developing an indigenous AI system to detect diminutive polyps in real-life scenarios that can be compatible with any high-definition colonoscopy and endoscopic video- capture software. METHODS We trained a masked region-based convolutional neural network model to detect and localize colonic polyps. Three independent datasets of colonoscopy videos comprising 1,039 image frames were used and divided into a training dataset of 688 frames and a testing dataset of 351 frames. Of 1,039 image frames, 231 were from real-life colonoscopy videos from our centre. The rest were from publicly available image frames already modified to be directly utilizable for developing the AI system. The image frames of the testing dataset were also augmented by rotating and zooming the images to replicate real-life distortions of images seen during colonoscopy. The AI system was trained to localize the polyp by creating a 'bounding box'. It was then applied to the testing dataset to test its accuracy in detecting polyps automatically. RESULTS The AI system achieved a mean average precision (equivalent to specificity) of 88.63% for automatic polyp detection. All polyps in the testing were identified by AI, i.e., no false-negative result in the testing dataset (sensitivity of 100%). The mean polyp size in the study was 5 (± 4) mm. The mean processing time per image frame was 96.4 minutes. CONCLUSIONS This AI system, when applied to real-life colonoscopy images, having wide variations in bowel preparation and small polyp size, can detect colonic polyps with a high degree of accuracy.
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Affiliation(s)
- Srijan Mazumdar
- Indian Institute of Liver and Digestive Sciences, Sitala (East), Jagadishpur, Sonarpur, 24 Parganas (South), Kolkata, 700 150, India.
| | - Saugata Sinha
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
| | - Saurabh Jha
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
| | - Balaji Jagtap
- Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440 010, India
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Dhaliwal J, Walsh CM. Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications. Gastrointest Endosc Clin N Am 2023; 33:291-308. [PMID: 36948747 DOI: 10.1016/j.giec.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.
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Affiliation(s)
- Jasbir Dhaliwal
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medictal Center, University of Cincinnati, OH, USA.
| | - Catharine M Walsh
- Division of Gastroenterology, Hepatology, and Nutrition, and the SickKids Research and Learning Institutes, The Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics and The Wilson Centre, University of Toronto, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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Gan P, Li P, Xia H, Zhou X, Tang X. The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going. GASTROENTEROLOGIA Y HEPATOLOGIA 2023; 46:203-213. [PMID: 35489584 DOI: 10.1016/j.gastrohep.2022.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023]
Abstract
Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.
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Affiliation(s)
- Peiling Gan
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Peiling Li
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Huifang Xia
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiaowei Tang
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
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The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy. Diagnostics (Basel) 2023; 13:diagnostics13040701. [PMID: 36832189 PMCID: PMC9955100 DOI: 10.3390/diagnostics13040701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.
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Xu H, Tang RSY, Lam TYT, Zhao G, Lau JYW, Liu Y, Wu Q, Rong L, Xu W, Li X, Wong SH, Cai S, Wang J, Liu G, Ma T, Liang X, Mak JWY, Xu H, Yuan P, Cao T, Li F, Ye Z, Shutian Z, Sung JJY. Artificial Intelligence-Assisted Colonoscopy for Colorectal Cancer Screening: A Multicenter Randomized Controlled Trial. Clin Gastroenterol Hepatol 2023; 21:337-346.e3. [PMID: 35863686 DOI: 10.1016/j.cgh.2022.07.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/29/2022] [Accepted: 07/05/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-assisted colonoscopy improves polyp detection and characterization in colonoscopy. However, data from large-scale multicenter randomized controlled trials (RCT) in an asymptomatic population are lacking. METHODS This multicenter RCT aimed to compare AI-assisted colonoscopy with conventional colonoscopy for adenoma detection in an asymptomatic population. Asymptomatic subjects 45-75 years of age undergoing colorectal cancer screening by direct colonoscopy or fecal immunochemical test were recruited in 6 referral centers in Hong Kong, Jilin, Inner Mongolia, Xiamen, and Beijing. In the AI-assisted colonoscopy, an AI polyp detection system (Eagle-Eye) with real-time notification on the same monitor of the endoscopy system was used. The primary outcome was overall adenoma detection rate (ADR). Secondary outcomes were mean number of adenomas per colonoscopy, ADR according to endoscopist's experience, and colonoscopy withdrawal time. This study received Institutional Review Board approval (CRE-2019.393). RESULTS From November 2019 to August 2021, 3059 subjects were randomized to AI-assisted colonoscopy (n = 1519) and conventional colonoscopy (n = 1540). Baseline characteristics and bowel preparation quality between the 2 groups were similar. The overall ADR (39.9% vs 32.4%; P < .001), advanced ADR (6.6% vs 4.9%; P = .041), ADR of expert (42.3% vs 32.8%; P < .001) and nonexpert endoscopists (37.5% vs 32.1%; P = .023), and adenomas per colonoscopy (0.59 ± 0.97 vs 0.45 ± 0.81; P < .001) were all significantly higher in the AI-assisted colonoscopy. The median withdrawal time (8.3 minutes vs 7.8 minutes; P = .004) was slightly longer in the AI-assisted colonoscopy group. CONCLUSIONS In this multicenter RCT in asymptomatic patients, AI-assisted colonoscopy improved overall ADR, advanced ADR, and ADR of both expert and nonexpert attending endoscopists. (ClinicalTrials.gov, Number: NCT04422548).
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Affiliation(s)
- Hong Xu
- Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China
| | - Raymond S Y Tang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Thomas Y T Lam
- Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China; JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong SAR, China; Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Guijun Zhao
- Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - James Y W Lau
- Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China; Department of Surgery, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yunpeng Liu
- Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Qi Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Long Rong
- Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Weiran Xu
- Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China
| | - Xue Li
- Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Sunny H Wong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Shuntian Cai
- Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Guanyi Liu
- Endoscopy Center, Peking University First Hospital, Beijing, China
| | - Tantan Ma
- Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China
| | - Xiong Liang
- Department of Endoscopy Center, Inner Mongolia Key Laboratory of Endoscopic Digestive Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Joyce W Y Mak
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hongzhi Xu
- Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Peng Yuan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Tingting Cao
- Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China
| | - Fudong Li
- Department of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Jilin, China
| | - Zhenshi Ye
- Department of Gastroenterology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Zhang Shutian
- Department of Gastroenterology and Hepatology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing, China
| | - Joseph J Y Sung
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China; Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong Kong SAR, China; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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Hamada Y, Tanaka K, Horiki N, Tsuboi J, Yamada R, Nakamura M, Tamaru S, Yamada T, Nakagawa H. Negative effect of prolonged cecal intubation time on adenoma detection in female patients. JGH Open 2023; 7:128-134. [PMID: 36852143 PMCID: PMC9958335 DOI: 10.1002/jgh3.12861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/25/2023]
Abstract
Background and Aim Withdrawal time of the colonoscope is associated with adenoma detection. However, the association between cecal intubation time and adenoma detection remains unclear. This study aimed to evaluate the association between cecal intubation time and adenoma detection. Methods This retrospective study analyzed prospectively collected data from a randomized controlled trial on female patients who underwent colonoscopy in an academic hospital. The primary outcome was the mean number of all adenomas detected per patient. Secondary outcomes included the mean number of advanced, diminutive, small/large, right-sided colonic, and left-sided colonic adenomas detected per patient. Furthermore, the detection rates of all categories of adenoma were evaluated. Results The analysis included 216 female patients aged ≥20 years. The correlation analysis did not reveal a significant relationship (P = 0.473) between cecal intubation and withdrawal times. The mean number of all adenomas detected per patient declined by approximately 30% (1.05-0.70) from the fastest to the slowest insertion time quartile. Adjusted regression analysis showed a significant decrease in the mean number of all adenomas detected per patient with increased intubation time (relative risk, RR = 0.87; 95% confidence interval, 0.76-0.99, P = 0.045), whereas the mean number of other categories of adenomas detected per patient and the detection rates of all categories of adenoma were not associated with the cecal intubation time. Conclusions This study showed a significant association between prolonged cecal intubation time and decreased adenoma detection. The cecal intubation time may be a significant quality indicator for colonoscopy.
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Affiliation(s)
- Yasuhiko Hamada
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Kyosuke Tanaka
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Noriyuki Horiki
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Junya Tsuboi
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Reiko Yamada
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Misaki Nakamura
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
| | - Satoshi Tamaru
- Clinical Research Support CenterMie University HospitalTsuJapan
| | - Tomomi Yamada
- Department of Medical InnovationOsaka University HospitalSuitaJapan
| | - Hayato Nakagawa
- Department of Gastroenterology and HepatologyMie University HospitalTsuJapan
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Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J, Sudarevic B, Zoller WG, Hann A, Puppe F. A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks. J Imaging 2023; 9:jimaging9020026. [PMID: 36826945 PMCID: PMC9967208 DOI: 10.3390/jimaging9020026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark.
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Affiliation(s)
- Adrian Krenzer
- Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Michael Banck
- Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Kevin Makowski
- Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
| | - Amar Hekalo
- Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
| | - Daniel Fitting
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Boban Sudarevic
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstrasse 60, 70174 Stuttgart, Germany
| | - Wolfgang G Zoller
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Kriegsbergstrasse 60, 70174 Stuttgart, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080 Würzburg, Germany
| | - Frank Puppe
- Department of Artificial Intelligence and Knowledge Systems, Julius-Maximilians University of Würzburg, Sanderring 2, 97070 Würzburg, Germany
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Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, Liu PX, Xiong F, Deng MM, Xia HF, Li JJ, Long XQ, Song Y, Li LP. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf) 2023; 11:goac081. [PMID: 36686571 PMCID: PMC9850273 DOI: 10.1093/gastro/goac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/21/2023] Open
Abstract
Background In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy. Methods A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4). Results A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001). Conclusions Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).
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Affiliation(s)
| | | | - Min Kang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Xue Peng
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Mei-Ling Shu
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Guan-Yu Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Pei-Xi Liu
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Ming-Ming Deng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Hong-Fen Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Jian-Jun Li
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Xiao-Qi Long
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Yan Song
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Liang-Ping Li
- Corresponding author. Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No.32 West Second Section, First Ring Road, Chengdu, Sichuan 610072, China. Tel: +86-28-8739 3927;
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Kumar K, Kumar P, Deb D, Unguresan ML, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel) 2023; 11:healthcare11020207. [PMID: 36673575 PMCID: PMC9859198 DOI: 10.3390/healthcare11020207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/01/2023] [Accepted: 01/04/2023] [Indexed: 01/13/2023] Open
Abstract
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
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Affiliation(s)
- Kamlesh Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Prince Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Dipankar Deb
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
- Correspondence:
| | | | - Vlad Muresan
- Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 2022; 5:184. [PMID: 36539473 PMCID: PMC9767933 DOI: 10.1038/s41746-022-00733-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.
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Affiliation(s)
- Sharib Ali
- grid.9909.90000 0004 1936 8403School of Computing, University of Leeds, LS2 9JT Leeds, UK
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Li MD, Huang ZR, Shan QY, Chen SL, Zhang N, Hu HT, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterol 2022; 22:517. [PMID: 36513975 PMCID: PMC9749329 DOI: 10.1186/s12876-022-02605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience. METHODS We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3. RESULTS A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05). CONCLUSION The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.
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Affiliation(s)
- Ming-De Li
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ze-Rong Huang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Quan-Yuan Shan
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Shu-Ling Chen
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ning Zhang
- grid.412615.50000 0004 1803 6239Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Wei Wang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
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Intelligent oncology: The convergence of artificial intelligence and oncology. JOURNAL OF THE NATIONAL CANCER CENTER 2022. [DOI: 10.1016/j.jncc.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Image-Enhanced Endoscopy Surveillance of Colon and Pouch Dysplasia in IBD. Dis Colon Rectum 2022; 65:S119-S128. [PMID: 35867688 DOI: 10.1097/dcr.0000000000002548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Patients with longstanding ulcerative colitis and Crohn's colitis are at risk for developing colorectal cancer and need regular endoscopic surveillance to detect and remove precursor lesions. To do so, different technologies are available. DATA SOURCES The sources are observational and controlled studies, meta-analysis, and expert consensus articles available on PubMed. STUDY SELECTION The selected materials include articles reporting outcomes of and recommendations on endoscopic surveillance and resection of dysplasia in the gastrointestinal tract, including the ileoanal pouch and the anal transition zone, in patients with inflammatory bowel disease. MAIN OUTCOME MEASURES Incidence and detection rate of dysplasia and cancer with different endoscopic techniques in patients with inflammatory bowel disease. RESULTS Risk of cancer is proportional to the duration and extent of the disease, and surveillance interval should be tailored on the individual risk in a range of 1 to 5 years. High-definition imaging and virtual chromoendoscopy have improved the detection of dysplasia and are now comparable with conventional dye spray chromoendoscopy. After restorative proctocolectomy with ileoanal pouch, the risk of cancer is modest, but its high mortality warrants endoscopic surveillance. The evidence to guide pouch surveillance is limited, and recently, the first expert consensus provided a framework of recommendations, which include an initial assessment 1 year after surgery and follow-up depending on individual risk factors. LIMITATIONS The limitation includes scarcity of data on ileoanal pouch surveillance. CONCLUSIONS Virtual chromoendoscopy and high-definition imaging have improved endoscopic surveillance, and more progress is expected with the implementation of artificial intelligence systems.
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Liu G, Zhao J, Tian G, Li S, Lu Y. Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis. Front Oncol 2022; 12:925924. [PMID: 36518311 PMCID: PMC9742812 DOI: 10.3389/fonc.2022.925924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/11/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field. METHODS The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis. RESULTS As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field's most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis. CONCLUSION In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
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Affiliation(s)
- Guangwei Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| | - Jun Zhao
- Department of Pharmacy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangye Tian
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
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