1
|
Ganeshan V, Bidwell J, Gyawali D, Nguyen TS, Morse J, Smith MP, Barton BM, McCoul ED. Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network. Int Forum Allergy Rhinol 2024. [PMID: 38853655 DOI: 10.1002/alr.23384] [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: 03/17/2024] [Revised: 05/14/2024] [Accepted: 05/23/2024] [Indexed: 06/11/2024]
Abstract
KEY POINTS A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.
Collapse
Affiliation(s)
- Vinayak Ganeshan
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
| | - Jonathan Bidwell
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
| | - Dipesh Gyawali
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
| | - Thinh S Nguyen
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
| | - Jonathan Morse
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
| | - Madeline P Smith
- Ochsner Clinical School, University of Queensland, New Orleans, Louisiana, USA
- Department of Otolaryngology, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Blair M Barton
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
- Ochsner Clinical School, University of Queensland, New Orleans, Louisiana, USA
- Department of Otolaryngology, Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Edward D McCoul
- Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA
- Ochsner Clinical School, University of Queensland, New Orleans, Louisiana, USA
- Department of Otolaryngology, Tulane University School of Medicine, New Orleans, Louisiana, USA
| |
Collapse
|
2
|
Paderno A, Rau A, Bedi N, Bossi P, Mercante G, Piazza C, Holsinger FC. Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer. Laryngoscope 2024. [PMID: 38850247 DOI: 10.1002/lary.31534] [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: 02/25/2024] [Revised: 04/15/2024] [Accepted: 05/06/2024] [Indexed: 06/10/2024]
Abstract
OBJECTIVES To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging. METHODS Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings. These embeddings served as input for a standard support vector machine (SVM) to classify the tissues as neoplastic or normal. The model's discriminative performance was validated using an 80-20 train-validation split. RESULTS From 38 endoscopic NBI videos, 327 image patches were analyzed. The classification results in the validation cohort demonstrated high accuracy (92%) and precision (89%), with a perfect recall (100%) and an F1-score of 94%. The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.96. CONCLUSION The use of large vision model-derived embeddings effectively differentiated between neoplastic and normal oropharyngeal tissues. This study supports the feasibility of employing CV foundation models like DINOv2 in the endoscopic evaluation of mucosal lesions, potentially augmenting diagnostic precision in Otorhinolaryngology. LEVEL OF EVIDENCE 4 Laryngoscope, 2024.
Collapse
Affiliation(s)
- Alberto Paderno
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Anita Rau
- Department of Biomedical Data Science, Stanford University, Palo Alto, California, U.S.A
| | - Nikita Bedi
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, California, U.S.A
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Oncology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Giuseppe Mercante
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology - Head and Neck Surgery, ASST Spedali Civili, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Floyd Christopher Holsinger
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, California, U.S.A
| |
Collapse
|
3
|
Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [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: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
Collapse
Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
| |
Collapse
|
4
|
Wang SX, Li Y, Zhu JQ, Wang ML, Zhang W, Tie CW, Wang GQ, Ni XG. The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images. Laryngoscope 2024; 134:127-135. [PMID: 37254946 DOI: 10.1002/lary.30781] [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: 08/07/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVE To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images. METHODS We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance. RESULTS The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists (p < 0.05). CONCLUSION The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses. LEVEL OF EVIDENCE 3 Laryngoscope, 134:127-135, 2024.
Collapse
Affiliation(s)
- Shi-Xu Wang
- Department of Head and Neck Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mei-Ling Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
5
|
Yang F, Huang N, Chen X, Wang M. Application of narrow band imaging and Lugol's iodine staining in screening for nasopharyngeal carcinoma. World J Surg Oncol 2023; 21:376. [PMID: 38037075 PMCID: PMC10687887 DOI: 10.1186/s12957-023-03258-5] [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: 08/02/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND To investigate the diagnostic value of conventional white light endoscopy (WLE), narrow band imaging (NBI) endoscopy, and Lugol's iodine staining under WLE (endoscopic iodine staining) in the screening and early diagnosis of nasopharyngeal carcinoma. METHODS Patients with nasopharyngeal lesions requiring biopsy attending the Department of Otolaryngology Head and Neck Surgery in our hospital between January 2021 and April 2023 were included in this study. Before biopsy, all subjects underwent conventional WLE, NBI endoscopy, and endoscopic iodine staining. On WLE, according to nasopharyngeal lesion morphology and color, patients were diagnosed with nasopharyngeal carcinoma ( +) or chronic hyperplastic nasopharyngitis (-). On NBI endoscopy, according to nasopharyngeal lesion vascular morphology, patients with type V manifestations (nasopharyngeal carcinoma) were categorized as NBI ( +) and patients with type I-IV manifestations (chronic hyperplastic nasopharyngitis) were categorized as NBI (-). Endoscopic iodine staining (1.6% Lugol's iodine solution) was positive ( +) if the mucosal surface was brown with no white patches, or negative (-) if there was no or light brown staining of the mucosal surface. Patients were divided into 2 groups based on histopathological diagnosis: nasopharyngeal carcinoma or chronic hyperplastic nasopharyngitis. Endoscopic diagnoses were compared with histopathological findings. The diagnostic performance of WLE, NBI endoscopy and endoscopic iodine staining for nasopharyngeal carcinoma were determined. RESULTS This study included 159 patients. On histopathology, 29 patients were diagnosed with nasopharyngeal carcinoma, and 130 patients were diagnosed with chronic hyperplastic nasopharyngitis. There were no significant differences in the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of conventional WLE, NBI endoscopy or endoscopic iodine staining for differentiating nasopharyngeal carcinoma and chronic hyperplastic nasopharyngitis. The diagnostic performance of the combination of conventional WLE, NBI endoscopy and endoscopic iodine staining was significantly improved compared to any procedure alone. CONCLUSIONS Conventional WLE, NBI endoscopy or endoscopic iodine staining had good diagnostic performance for differentiating nasopharyngeal carcinoma and chronic hyperplastic nasopharyngitis. In particular, NBI endoscopy and endoscopic iodine staining alone or combined had clinical utility for identifying patients with nasopharyngeal lesions that are eligible for a watch-and-wait strategy.
Collapse
Affiliation(s)
- Fan Yang
- Department of Otorhinolaryngology Head and Neck, Fuzong Clinical College of Fujian Medical University, the 900th Hospital of Joint Logistic Support Force of PLA, 156 West Second Ring North Road, Fuzhou, 0591, China
| | - Ning Huang
- Department of Otorhinolaryngology Head and Neck, Fuzong Clinical College of Fujian Medical University, the 900th Hospital of Joint Logistic Support Force of PLA, 156 West Second Ring North Road, Fuzhou, 0591, China
| | - Xianming Chen
- Department of Otorhinolaryngology Head and Neck, Fuzong Clinical College of Fujian Medical University, the 900th Hospital of Joint Logistic Support Force of PLA, 156 West Second Ring North Road, Fuzhou, 0591, China.
| | - Maoxin Wang
- Department of Otorhinolaryngology Head and Neck, Fuzong Clinical College of Fujian Medical University, the 900th Hospital of Joint Logistic Support Force of PLA, 156 West Second Ring North Road, Fuzhou, 0591, China.
| |
Collapse
|
6
|
He Z, Zhang K, Zhao N, Wang Y, Hou W, Meng Q, Li C, Chen J, Li J. Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy. iScience 2023; 26:107463. [PMID: 37720094 PMCID: PMC10502364 DOI: 10.1016/j.isci.2023.107463] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/07/2023] [Accepted: 07/20/2023] [Indexed: 09/19/2023] Open
Abstract
Nasopharyngeal carcinoma (NPC) is known for high curability during early stage of the disease, and early diagnosis relies on nasopharyngeal endoscopy and subsequent pathological biopsy. To enhance the early diagnosis rate by aiding physicians in the real-time identification of NPC and directing biopsy site selection during endoscopy, we assembled a dataset comprising 2,429 nasopharyngeal endoscopy video frames from 690 patients across three medical centers. With these data, we developed a deep learning-based NPC detection model using the you only look once (YOLO) network. Our model demonstrated high performance, with precision, recall, mean average precision, and F1-score values of 0.977, 0.943, 0.977, and 0.960, respectively, for internal test set and 0.825, 0.743, 0.814, and 0.780 for external test set at 0.5 intersection over union. Remarkably, our model demonstrated a high inference speed (52.9 FPS), surpassing the average frame rate (25.0 FPS) of endoscopy videos, thus making real-time detection in endoscopy feasible.
Collapse
Affiliation(s)
- Zicheng He
- Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou Key Laboratory of Otorhinolaryngology, Otorhinolaryngology Institute of Sun Yat-sen University, Guangzhou 510080, P.R.China
- Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning 530000, P.R.China
| | - Kai Zhang
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R.China
| | - Nan Zhao
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R.China
| | - Yongquan Wang
- Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou Key Laboratory of Otorhinolaryngology, Otorhinolaryngology Institute of Sun Yat-sen University, Guangzhou 510080, P.R.China
| | | | - Qinxiang Meng
- Guangzhou First People’s Hospital, Guangzhou 510180, P.R.China
| | - Chunwei Li
- Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou Key Laboratory of Otorhinolaryngology, Otorhinolaryngology Institute of Sun Yat-sen University, Guangzhou 510080, P.R.China
| | - Junzhou Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, P.R.China
| | - Jian Li
- Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou Key Laboratory of Otorhinolaryngology, Otorhinolaryngology Institute of Sun Yat-sen University, Guangzhou 510080, P.R.China
- Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning 530000, P.R.China
| |
Collapse
|
7
|
Sampieri C, Baldini C, Azam MA, Moccia S, Mattos LS, Vilaseca I, Peretti G, Ioppi A. Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review. Otolaryngol Head Neck Surg 2023; 169:811-829. [PMID: 37051892 DOI: 10.1002/ohn.343] [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: 12/29/2022] [Revised: 03/03/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVE The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. DATA SOURCES Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. REVIEW METHODS A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. CONCLUSIONS Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. IMPLICATIONS FOR PRACTICE This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.
Collapse
Affiliation(s)
- Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain
| | - Chiara Baldini
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Sara Moccia
- Department of Excellence in Robotics and AI, The BioRobotics Institute, Pisa, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Isabel Vilaseca
- Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain
- Head Neck Clínic, Agència de Gestió d'Ajuts Universitaris i de Recerca, Barcelona, Catalunya, Spain
- Surgery and Medical-Surgical Specialties Department, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
- Translational Genomics and Target Therapies in Solid Tumors Group, Faculty of Medicine, Institut d́Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- University of Barcelona, Barcelona, Spain
| | - Giorgio Peretti
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Alessandro Ioppi
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| |
Collapse
|
8
|
Yang X, Wu J, Chen X. Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma. J Clin Med 2023; 12:jcm12093077. [PMID: 37176518 PMCID: PMC10178972 DOI: 10.3390/jcm12093077] [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: 02/19/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.
Collapse
Affiliation(s)
- Xinggang Yang
- Division of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Juan Wu
- Out-Patient Department, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Guoxue Road 37, Chengdu 610041, China
| |
Collapse
|
9
|
Zhu JQ, Wang ML, Li Y, Zhang W, Li LJ, Liu L, Zhang Y, Han CJ, Tie CW, Wang SX, Wang GQ, Ni XG. Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study. Am J Otolaryngol 2023; 44:103695. [PMID: 36473265 DOI: 10.1016/j.amjoto.2022.103695] [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: 08/26/2022] [Revised: 09/26/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-driven intelligent laryngoscopy monitoring assistant (ILMA) for landmark anatomical sites identification on laryngoscopic images and videos based on a convolutional neural network (CNN). MATERIALS AND METHODS The laryngoscopic images taken from January to December 2018 were retrospectively collected, and ILMA was developed using the CNN model of Inception-ResNet-v2 + Squeeze-and-Excitation Networks (SENet). A total of 16,000 laryngoscopic images were used for training. These were assigned to 20 landmark anatomical sites covering six major head and neck regions. In addition, the performance of ILMA in identifying anatomical sites was validated using 4000 laryngoscopic images and 25 videos provided by five other tertiary hospitals. RESULTS ILMA identified the 20 anatomical sites on the laryngoscopic images with a total accuracy of 97.60 %, and the average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 100 %, 99.87 %, 97.65 %, and 99.87 %, respectively. In addition, multicenter clinical verification displayed that the accuracy of ILMA in identifying the 20 targeted anatomical sites in 25 laryngoscopic videos from five hospitals was ≥95 %. CONCLUSION The proposed CNN-based ILMA model can rapidly and accurately identify the anatomical sites on laryngoscopic images. The model can reflect the coverage of anatomical regions of the head and neck by laryngoscopy, showing application potential in improving the quality of laryngoscopy.
Collapse
Affiliation(s)
- Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mei-Ling Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Li-Juan Li
- Department of Otorhinolaryngology, The People's Hospital of Wenshan Prefecture, Wenshan, Yunnan, China
| | - Lin Liu
- Department of Otolaryngology-Head and Neck Surgery, Dalian Municipal Friendship Hospital, Dalian, Liaoning, China
| | - Yan Zhang
- Department of Otorhinolaryngology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Cai-Juan Han
- Department of Otolaryngology-Head and Neck Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi-Xu Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| |
Collapse
|
10
|
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: 4] [Impact Index Per Article: 2.0] [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.
Collapse
Affiliation(s)
- Sharib Ali
- grid.9909.90000 0004 1936 8403School of Computing, University of Leeds, LS2 9JT Leeds, UK
| |
Collapse
|
11
|
Lee VHF, Adham M, Ben Kridis W, Bossi P, Chen MY, Chitapanarux I, Gregoire V, Hao SP, Ho C, Ho GF, Kannarunimit D, Kwong DLW, Lam KO, Lam WKJ, Le QT, Lee AWM, Lee NY, Leung TW, Licitra L, Lim DWT, Lin JC, Loh KS, Lou PJ, Machiels JP, Mai HQ, Mesía R, Ng WT, Ngan RKC, Tay JK, Tsang RKY, Tong CC, Wang HM, Wee JT. International recommendations for plasma Epstein-Barr virus DNA measurement in nasopharyngeal carcinoma in resource-constrained settings: lessons from the COVID-19 pandemic. Lancet Oncol 2022; 23:e544-e551. [PMID: 36455583 PMCID: PMC9704820 DOI: 10.1016/s1470-2045(22)00505-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 11/30/2022]
Abstract
The effects of the COVID-19 pandemic continue to constrain health-care staff and resources worldwide, despite the availability of effective vaccines. Aerosol-generating procedures such as endoscopy, a common investigation tool for nasopharyngeal carcinoma, are recognised as a likely cause of SARS-CoV-2 spread in hospitals. Plasma Epstein-Barr virus (EBV) DNA is considered the most accurate biomarker for the routine management of nasopharyngeal carcinoma. A consensus statement on whether plasma EBV DNA can minimise the need for or replace aerosol-generating procedures, imaging methods, and face-to-face consultations in managing nasopharyngeal carcinoma is urgently needed amid the current pandemic and potentially for future highly contagious airborne diseases or natural disasters. We completed a modified Delphi consensus process of three rounds with 33 international experts in otorhinolaryngology or head and neck surgery, radiation oncology, medical oncology, and clinical oncology with vast experience in managing nasopharyngeal carcinoma, representing 51 international professional societies and national clinical trial groups. These consensus recommendations aim to enhance consistency in clinical practice, reduce ambiguity in delivering care, and offer advice for clinicians worldwide who work in endemic and non-endemic regions of nasopharyngeal carcinoma, in the context of COVID-19 and other airborne pandemics, and in future unexpected settings of severe resource constraints and insufficiency of personal protective equipment.
Collapse
Affiliation(s)
- Victor Ho-Fun Lee
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China,Correspondence to:Dr Victor Ho-Fun Lee, Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Marlinda Adham
- Department of Otorhinolaryngology–Head and Neck Surgery, Faculty of Medicine, Universitas Indonesia–Cipto Mangunkusumo Hospital, Jakarta, Indonesia
| | - Wala Ben Kridis
- Department of Medical Oncology, Habib Bourguiba Hospital, University of Sfax, Sfax, Tunisia
| | - Paolo Bossi
- Medical Oncology Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health–Medical Oncology, University of Brescia, ASST–Spedali Civili, Brescia, Italy,Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy
| | - Ming-Yuan Chen
- Department of Nasopharyngeal Carcinoma, Sun Yat–sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Imjai Chitapanarux
- Division of Radiation Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Vincent Gregoire
- Department of Radiation Oncology, Centre Léon Bérard, Lyon, France
| | - Sheng Po Hao
- Department of Otolaryngology, Shin Kong Wu Ho–Su Memorial Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheryl Ho
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Gwo Fuang Ho
- Clinical Oncology Unit, University Malaya Cancer Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Danita Kannarunimit
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Dora Lai-Wan Kwong
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Ka-On Lam
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Wai Kei Jacky Lam
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China,Department of Chemical Pathology, State Key Laboratory of Translational Oncology, and Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong Special Administrative Region, China
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Anne Wing-Mui Lee
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - To-Wai Leung
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Lisa Licitra
- Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy,Department of Oncology and Hemato–Oncology, University of Milan, Milan, Italy
| | | | - Jin-Ching Lin
- Department of Radiation Oncology, Changhua Christian Hospital, Changhua, Taiwan
| | - Kwok Seng Loh
- Department of Otolaryngology–Head & Neck Surgery, National University of Singapore, Singapore
| | - Pei-Jen Lou
- Department of Otolaryngology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan,Graduate Institute of Anatomy and Cell Biology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jean-Pascal Machiels
- Service d'Oncologie Médicale, Institut Roi Albert II, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Hai-Qiang Mai
- Department of Nasopharyngeal Carcinoma, Sun Yat–sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Ricard Mesía
- Medical Oncology Department, Catalan Institute of Oncology–Badalona, B–ARGO Group, IGTP, Badalona, Spain
| | - Wai-Tong Ng
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Roger Kai-Cheong Ngan
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Clinical Oncology Center, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Joshua K Tay
- Department of Otolaryngology–Head & Neck Surgery, National University of Singapore, Singapore
| | - Raymond King-Yin Tsang
- Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Department of Otolaryngology–Head & Neck Surgery, National University of Singapore, Singapore
| | - Chi-Chung Tong
- Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Hung-Ming Wang
- Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan
| | - Joseph T Wee
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| |
Collapse
|
12
|
Ji L, Mao R, Wu J, Ge C, Xiao F, Xu X, Xie L, Gu X. Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12102478. [PMID: 36292167 PMCID: PMC9601165 DOI: 10.3390/diagnostics12102478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/03/2022] [Accepted: 10/09/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.
Collapse
Affiliation(s)
- Li Ji
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Rongzhi Mao
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Jian Wu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Cheng Ge
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Feng Xiao
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| | - Xiaofeng Gu
- Department of Otorhinolaryngology, The Second People’s Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003, China
- Correspondence: (X.X.); (L.X.); (X.G.)
| |
Collapse
|