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Rokhshad R, Mohammad-Rahimi H, Sohrabniya F, Jafari B, Shobeiri P, Tsolakis IA, Ourang SA, Sultan AS, Khawaja SN, Bavarian R, Palomo JM. Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis. J Oral Rehabil 2024. [PMID: 38757865 DOI: 10.1111/joor.13701] [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: 08/16/2023] [Revised: 02/20/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND AND OBJECTIVE The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. MATERIALS AND METHODS An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. RESULTS Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. CONCLUSION Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland, USA
| | - Fatemeh Sohrabniya
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Bahare Jafari
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Parnian Shobeiri
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Ioannis A Tsolakis
- Department of Orthodontics, School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Seyed AmirHossein Ourang
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmed S Sultan
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Shehryar Nasir Khawaja
- Orofacial Pain Medicine, Shaukat Khanum Memorial Cancer Hospitals and Research Centres, Lahore and Peshawar, Pakistan
- School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
| | - Roxanne Bavarian
- Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - Juan Martin Palomo
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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Rokhshad R, Zhang P, Mohammad-Rahimi H, Pitchika V, Entezari N, Schwendicke F. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study. J Dent 2024; 144:104938. [PMID: 38499280 DOI: 10.1016/j.jdent.2024.104938] [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: 11/11/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry. METHODS Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency. RESULTS Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7). CLINICAL SIGNIFICANCE Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making. CONCLUSION In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.
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Affiliation(s)
- Rata Rokhshad
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Ping Zhang
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Vinay Pitchika
- Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
| | - Niloufar Entezari
- Department of pediatric dentistry, School of Dentistry, Qom University of Medical Sciences, Qom, Iran
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
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Alghazo J, Latif G. AI/ML-Based Medical Image Processing and Analysis. Diagnostics (Basel) 2023; 13:3671. [PMID: 38132255 PMCID: PMC10742629 DOI: 10.3390/diagnostics13243671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
The medical field is experiencing remarkable advancements, notably with the latest technologies-artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)-that are in place to offer groundbreaking solutions to support medical professionals in the diagnostic process [...].
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Affiliation(s)
- Jaafar Alghazo
- Artificial Intelligence Research Initiative, College of Engineering and Mines, University of North Dakota, Grand Forks, ND 58202, USA;
| | - Ghazanfar Latif
- Computer Science Department, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
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Cai Y, Xie Y, Zhang S, Wang Y, Wang Y, Chen J, Huang Z. Prediction of postoperative recurrence of oral cancer by artificial intelligence model: Multilayer perceptron. Head Neck 2023; 45:3053-3066. [PMID: 37789719 DOI: 10.1002/hed.27533] [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: 04/06/2023] [Revised: 09/06/2023] [Accepted: 09/17/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Postoperative recurrence of oral cancer is an important factor affecting the prognosis of patients. Artificial intelligence is used to establish a machine learning model to predict the risk of postoperative recurrence of oral cancer. METHODS The information of 387 patients with postoperative oral cancer were collected to establish the multilayer perceptron (MLP) model. The comprehensive variable model was compared with the characteristic variable model, and the MLP model was compared with other models to evaluate the sensitivity of different models in the prediction of postoperative recurrence of oral cancer. RESULTS The overall performance of the MLP model under comprehensive variable input was the best. CONCLUSION The MLP model has good sensitivity to predict postoperative recurrence of oral cancer, and the predictive model with variable input training is better than that with characteristic variable input.
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Affiliation(s)
- Yongkang Cai
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Yutong Xie
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Shulian Zhang
- School of Software Engineering, South China University of Technology, Guangzhou, China
| | - Yuepeng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Yan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Jian Chen
- School of Software Engineering, South China University of Technology, Guangzhou, China
| | - Zhiquan Huang
- Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China
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Mahesh Batra A, Reche A. A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment. Cureus 2023; 15:e49319. [PMID: 38143639 PMCID: PMC10748804 DOI: 10.7759/cureus.49319] [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: 11/09/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.
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Affiliation(s)
- Aastha Mahesh Batra
- Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Amit Reche
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Hirsch JM, Sandy R, Hasséus B, Lindblad J. A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma. J Oral Pathol Med 2023; 52:826-833. [PMID: 37710407 DOI: 10.1111/jop.13484] [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: 03/31/2023] [Revised: 07/05/2023] [Accepted: 08/06/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Oral squamous cell carcinoma (OSCC) is a widespread disease with only 50%-60% 5-year survival. Individuals with potentially malignant precursor lesions are at high risk. METHODS Survival could be increased by effective, affordable, and simple screening methods, along with a shift from incisional tissue biopsies to non-invasive brush biopsies for cytology diagnosis, which are easy to perform in primary care. Along with the explainable, fast, and objective artificial intelligence characterisation of cells through deep learning, an easy-to-use, rapid, and cost-effective methodology for finding high-risk lesions is achievable. The collection of cytology samples offers the further opportunity of explorative genomic analysis. RESULTS Our prospective multicentre study of patients with leukoplakia yields a vast number of oral keratinocytes. In addition to cytopathological analysis, whole-slide imaging and the training of deep neural networks, samples are analysed according to a single-cell RNA sequencing protocol, enabling mapping of the entire keratinocyte transcriptome. Mapping the changes in the genetic profile, based on mRNA expression, facilitates the identification of biomarkers that predict cancer transformation. CONCLUSION This position paper highlights non-invasive methods for identifying patients with oral mucosal lesions at risk of malignant transformation. Reliable non-invasive methods for screening at-risk individuals bring the early diagnosis of OSCC within reach. The use of biomarkers to decide on a targeted therapy is most likely to improve the outcome. With the large-scale collection of samples following patients over time, combined with genomic analysis and modern machine-learning-based approaches for finding patterns in data, this path holds great promise.
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Affiliation(s)
- Jan-Michaél Hirsch
- Department of Surgical Sciences, Oral and Maxillofacial Surgery, Medical Faculty, Uppsala University, Uppsala, Sweden
- Department of Research, Development and Education, Public Dental Services, Stockholm, Sweden
| | - Ronak Sandy
- Eastmaninstitutet, Department of Oral and Maxillofacial Surgery, Public Dental Services, Folktandvården Stockholm, Stockholm, Sweden
| | - Bengt Hasséus
- Department of Oral Medicine and Pathology, Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joakim Lindblad
- Computerised Image Processing, Department of Information Technology, Centre for Image Analysis, Uppsala University, Uppsala, Sweden
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Banavar G, Ogundijo O, Julian C, Toma R, Camacho F, Torres PJ, Hu L, Chandra T, Piscitello A, Kenny L, Vasani S, Batstone M, Dimitrova N, Vuyisich M, Amar S, Punyadeera C. Detecting salivary host and microbiome RNA signature for aiding diagnosis of oral and throat cancer. Oral Oncol 2023; 145:106480. [PMID: 37454545 DOI: 10.1016/j.oraloncology.2023.106480] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/16/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE Oral squamous cell carcinoma (OSCC) and oropharyngeal squamous cell carcinoma (OPSCC) can go undetected resulting in late detection and poor outcomes. We describe the development and validation of CancerDetect for Oral & Throat cancer™ (CDOT), to detect markers of OSCC and/or OPSCC within a high-risk population. MATERIAL AND METHODS We collected saliva samples from 1,175 individuals who were 50 years or older, or adults with a tobacco use history. 945 of those were used to train a classifier using machine learning methods, resulting in a salivary microbial and human metatranscriptomic signature. The classifier was then independently validated on the 230 remaining samples prospectively collected and unseen by the classifier, consisting of 20 OSCC (all stages), 76 OPSCC (all stages), and 134 negatives (including 14 pre-malignant). RESULTS On the validation cohort, the specificity of the CDOT test was 94 %, sensitivity was 90 % for participants with OSCC, and 84.2 % for participants with OPSCC. Similar classification results were observed among people in early stage (stages I & II) vs late stage (stages III & IV). CONCLUSIONS CDOT is a non-invasive test that can be easily administered in dentist offices, primary care centres and specialised cancer clinics for early detection of OPSCC and OSCC. This test, having received FDA's breakthrough designation for accelerated review, has the potential to enable early diagnosis, saving lives and significantly reducing healthcare expenditure.
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Affiliation(s)
- Guruduth Banavar
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA.
| | - Oyetunji Ogundijo
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | - Cristina Julian
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | - Ryan Toma
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | - Francine Camacho
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | - Pedro J Torres
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | - Lan Hu
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | | | | | - Liz Kenny
- Royal Brisbane and Women's Hospital and The School of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Sarju Vasani
- Department of Otolaryngology, Royal Brisbane and Women's Hospital and Faculty of Medicine, University of Queensland, Herston, QLD, Australia
| | - Martin Batstone
- Oral and Maxillofacial Surgery Department, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
| | | | - Momchilo Vuyisich
- Viome Research Institute, Viome Life Sciences Inc, New York City, NY, and Seattle, WA, USA
| | | | - Chamindie Punyadeera
- The Saliva and Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia; Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia.
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Liyanage V, Tao M, Park JS, Wang KN, Azimi S. Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks. J Dent 2023; 137:104657. [PMID: 37574105 DOI: 10.1016/j.jdent.2023.104657] [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: 07/04/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
OBJECTIVES Given the increasing incidence of oral cancer, it is essential to provide high-risk communities, especially in remote regions, with an affordable, user-friendly tool for visual lesion diagnosis. This proof-of-concept study explored the utility and feasibility of a smartphone application that can photograph and diagnose oral lesions. METHODS The images of oral lesions with confirmed diagnoses were sourced from oral and maxillofacial textbooks. In total, 342 images were extracted, encompassing lesions from various regions of the oral cavity such as the gingiva, palate, and labial mucosa. The lesions were segregated into three categories: Class 1 represented non-neoplastic lesions, Class 2 included benign neoplasms, and Class 3 contained premalignant/malignant lesions. The images were analysed using MobileNetV3 and EfficientNetV2 models, with the process producing an accuracy curve, confusion matrix, and receiver operating characteristic (ROC) curve. RESULTS The EfficientNetV2 model showed a steep increase in validation accuracy early in the iterations, plateauing at a score of 0.71. According to the confusion matrix, this model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions was 64% and 80% respectively. Conversely, the MobileNetV3 model exhibited a more gradual increase, reaching a plateau at a validation accuracy of 0.70. The MobileNetV3 model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions, according to the confusion matrix, was 64% and 82% respectively. CONCLUSIONS Our proof-of-concept study effectively demonstrated the potential accuracy of AI software in distinguishing malignant lesions. This could play a vital role in remote screenings for populations with limited access to dental practitioners. However, the discrepancies between the classification of images and the results of "non-malignant lesions" calls for further refinement of the models and the classification system used. CLINICAL SIGNIFICANCE The findings of this study indicate that AI software has the potential to aid in the identification or screening of malignant oral lesions. Further improvements are required to enhance accuracy in classifying non-malignant lesions.
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Affiliation(s)
- Viduni Liyanage
- International Research Collaborative - Oral Health and Equity, The University of Western Australia, Crawley, Western Australia, Australia; UWA Dental School, The University of Western Australia, Nedlands, Western Australia, Australia
| | - Mengqiu Tao
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Joon Soo Park
- International Research Collaborative - Oral Health and Equity, The University of Western Australia, Crawley, Western Australia, Australia; UWA Dental School, The University of Western Australia, Nedlands, Western Australia, Australia; School of Engineering, Information Technology and Physical Sciences, Federation University, Ballarat, Victoria, Australia.
| | - Kate N Wang
- School of Biomedical and Health Sciences, Royal Melbourne Institute of Technology, Bundoora, Victoria, Australia
| | - Somayyeh Azimi
- International Research Collaborative - Oral Health and Equity, The University of Western Australia, Crawley, Western Australia, Australia
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Y D, Ramalingam K, Ramani P, Mohan Deepak R. Machine Learning in the Detection of Oral Lesions With Clinical Intraoral Images. Cureus 2023; 15:e44018. [PMID: 37753028 PMCID: PMC10519616 DOI: 10.7759/cureus.44018] [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: 08/12/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION Artificial intelligence in oncology has gained a lot of interest in recent years. Early detection of Oral squamous cell carcinoma (OSCC) is crucial for early management to attain a better prognosis and overall survival. Machine learning (ML) has also been used in oral cancer studies to explore the discrimination between clinically normal and oral cancer. MATERIALS AND METHODS A dataset comprising 360 clinical intra-oral images of OSCC, Oral Potentially Malignant Disorders (OPMDs) and clinically healthy oral mucosa were used. Clinicians trained the machine learning model with the clinical images (n=300). Roboflow software (Roboflow Inc, USA) was used to classify and annotate images along with Multi-class annotation and object detection models trained by two expert oral pathologists. The test dataset (n=60) of new clinical images was again evaluated by two clinicians and Roboflow. The results were tabulated and Kappa statistics was performed using SPSS v23.0 (IBM Corp., Armonk, NY). Results: Training dataset clinical images (n=300) were used to train the clinicians and Roboflow algorithm. The test dataset (n=60) of new clinical images was again evaluated by the clinicians and Roboflow. The observed outcomes revealed that the Mean Average Precision (mAP) was 25.4%, precision 29.8% and Recall 32.9%. Based on the kappa statistical analysis the 0.7 value shows a moderate agreement between the clinicians and the machine learning model. The test dataset showed the specificity and sensitivity of the Roboflow machine learning model to be 75% and 88.9% respectively. Conclusion: In conclusion, machine learning showed promising results in the early detection of suspected lesions using clinical intraoral images and aids general dentists and patients in the detection of suspected lesions such as OPMDs and OSCC that require biopsy and immediate treatment.
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Affiliation(s)
- Dinesh Y
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Karthikeyan Ramalingam
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Pratibha Ramani
- Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Ramya Mohan Deepak
- Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
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Rokhshad R, Ducret M, Chaurasia A, Karteva T, Radenkovic M, Roganovic J, Hamdan M, Mohammad-Rahimi H, Krois J, Lahoud P, Schwendicke F. Ethical considerations on artificial intelligence in dentistry: A framework and checklist. J Dent 2023; 135:104593. [PMID: 37355089 DOI: 10.1016/j.jdent.2023.104593] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
| | - Maxime Ducret
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Faculty of Odontology, University Claude Bernard Lyon Il, University of Lyon, Lyon, France
| | - Akhilanand Chaurasia
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India; Faculty of Dentistry, University of Puthisashtra, Combodia
| | - Teodora Karteva
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University Plovdiv, Bulgaria
| | - Miroslav Radenkovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Serbia
| | - Jelena Roganovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology in Dentistry, School of Dental medicine, University of Belgrade, Serbia
| | - Manal Hamdan
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; General Dental Sciences Department, Marquette University School of Dentistry, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pierre Lahoud
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Oral and MaxilloFacial Surgery & Imaging and Pathology- OMFS-IMPATH Research Group, KU Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Belgium
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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12
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Khanagar SB, Alkadi L, Alghilan MA, Kalagi S, Awawdeh M, Bijai LK, Vishwanathaiah S, Aldhebaib A, Singh OG. Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review. Biomedicines 2023; 11:1612. [PMID: 37371706 DOI: 10.3390/biomedicines11061612] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/27/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models' abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.
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Affiliation(s)
- Sanjeev B Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Lubna Alkadi
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Maryam A Alghilan
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Sara Kalagi
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Mohammed Awawdeh
- Preventive Dental Science Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Lalitytha Kumar Bijai
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Maxillofacial Surgery and Diagnostic Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pediatric Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
| | - Ali Aldhebaib
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
| | - Oinam Gokulchandra Singh
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
- Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
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13
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Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus 2023; 15:e41227. [PMID: 37529520 PMCID: PMC10387377 DOI: 10.7759/cureus.41227] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.
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Affiliation(s)
- Ashwini Dhopte
- Department of Oral Medicine and Radiology, Rama Dental College and Research Centre, Kanpur, IND
| | - Hiroj Bagde
- Department of Periodontology, Rama Dental College and Research Centre, Kanpur, IND
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Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S, Orhan K. Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 2023; 13:diagnostics13081487. [PMID: 37189586 DOI: 10.3390/diagnostics13081487] [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: 12/22/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). METHODS 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. RESULTS The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. CONCLUSIONS Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.
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Affiliation(s)
- Emel Gardiyanoğlu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
- DESAM Institute, Near East University, 99138 Nicosia, Cyprus
| | - Nurullah Akkaya
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, 99138 Nicosia, Cyprus
| | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey
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15
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Dixit S, Kumar A, Srinivasan K. A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13071353. [PMID: 37046571 PMCID: PMC10093759 DOI: 10.3390/diagnostics13071353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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Janta S, Suwannakhan A, Yurasakpong L, Chaiyamoon A, Kruepunga N, Iwanaga J, Tubbs RS, Eiamratchanee P, Paensukyen T. Anatomical Variants Identified on Computed Tomography of Oropharyngeal Carcinoma Patients. Medicina (B Aires) 2023; 59:medicina59040707. [PMID: 37109665 PMCID: PMC10144055 DOI: 10.3390/medicina59040707] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/28/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023] Open
Abstract
Background and Objectives: Anatomical variations in the head, neck and chest are common, and are observed as occasional findings on computed tomography (CT). Although anatomical variations are mostly asymptomatic and do not cause any negative influence on the body function, they may jeopardize diagnosis and may be confused with pathological conditions. The presence of variations may also limit surgical access during tumor removal. The aim of this study was to investigate the prevalence of six anatomical variations—os acromiale, episternal ossicles, cervical rib, Stafne bone cavity, azygos lobe and tracheal bronchus—in an open-access computed tomography dataset obtained from oropharyngeal cancer patients. Materials and Methods: A total of 606 upper-chest and neck computed-tomography scans (79.4% male and 20.6% female) were retrospectively investigated. Sex difference was evaluated using the z-test for two proportions. Results: Os acromiale, episternal ossicles, cervical rib, Stafne bone cavity, azygos lobe, and tracheal bronchus were present in 3.1%, 2.2%, 0.2%, 0%, 0.3% and 0.5%, respectively, of all patients. Os acromiale was identified as meso-acromion in 86.6%, and as pre-acromion in 17.4%, of all acromia. Episternal ossicles were present unilaterally in 58.3%, and bilaterally in 41.7%, of all sterna. Only the cervical rib showed a sex difference in prevalence. Conclusions: awareness of these variations is important for radiologists interpreting head, neck and chest CTs; for example, those of oropharyngeal cancer patients. This study also illustrates the applicability of publicly available datasets in prevalence-based anatomical research. While most of the variations investigated in the present study are well-known, the episternal ossicles are not well explored, and need further investigation.
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Affiliation(s)
- Sirorat Janta
- Anatomy Unit, Department of Medical Science, Faculty of Science, Rangsit University, Pathum Thani 12000, Thailand
| | - Athikhun Suwannakhan
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- In Silico and Clinical Anatomy Research Group (iSCAN), Bangkok 10400, Thailand
| | - Laphatrada Yurasakpong
- Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok 10210, Thailand
| | - Arada Chaiyamoon
- Department of Anatomy, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Nutmethee Kruepunga
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
- In Silico and Clinical Anatomy Research Group (iSCAN), Bangkok 10400, Thailand
| | - Joe Iwanaga
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Neurology, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Anatomy, Kurume University School of Medicine, Fukuoka 830-0011, Japan
- Department of Structural and Cellular Biology, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Neurosurgery, Ochsner Neuroscience Institute, Ochsner Health System, New Orleans, LA 70112, USA
| | - R. Shane Tubbs
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Anatomy, Kurume University School of Medicine, Fukuoka 830-0011, Japan
- Department of Structural and Cellular Biology, Tulane University School of Medicine, New Orleans, LA 70112, USA
- Department of Neurosurgery, Ochsner Neuroscience Institute, Ochsner Health System, New Orleans, LA 70112, USA
- Department of Anatomical Sciences, St. George’s University, St. George’s FZ818, Grenada
| | - Pinthusorn Eiamratchanee
- Department of Anatomical Sciences, St. George’s University, St. George’s FZ818, Grenada
- St. George’s International School of Medicine Keith B. Taylor Global Scholars Program, Northumbria University, Newcastle-upon-Tyne NE7 7XA, UK
| | - Tawanrat Paensukyen
- Biomedical Science Program, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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de Souza LL, Fonseca FP, Araújo ALD, Lopes MA, Vargas PA, Khurram SA, Kowalski LP, Dos Santos HT, Warnakulasuriya S, Dolezal J, Pearson AT, Santos-Silva AR. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. J Oral Pathol Med 2023; 52:197-205. [PMID: 36792771 DOI: 10.1111/jop.13414] [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: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 02/17/2023]
Abstract
Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.
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Affiliation(s)
- Lucas Lacerda de Souza
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Felipe Paiva Fonseca
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
- Department of Oral Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marcio Ajudarte Lopes
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
| | - Syed Ali Khurram
- Unit of Oral & Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery, University of Sao Paulo Medical School and Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil
| | - Harim Tavares Dos Santos
- Department of Otolaryngology-Head and Neck Surgery, University of Missouri, Columbia, Missouri, USA
- Department of Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Saman Warnakulasuriya
- King's College London, London, UK
- WHO Collaborating Centre for Oral Cancer, London, UK
| | - James Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Alan Roger Santos-Silva
- Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), São Paulo, Brazil
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Gomes RFT, Schmith J, de Figueiredo RM, Freitas SA, Machado GN, Romanini J, Carrard VC. Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20053894. [PMID: 36900902 PMCID: PMC10002140 DOI: 10.3390/ijerph20053894] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVES Artificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images. METHOD The CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion. RESULTS A total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset. CONCLUSIONS We reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
| | - Jean Schmith
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
- Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Rodrigo Marques de Figueiredo
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
- Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Samuel Armbrust Freitas
- Department of Applied Computing, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Giovanna Nunes Machado
- Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
| | - Juliana Romanini
- Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil
| | - Vinicius Coelho Carrard
- Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
- Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil
- TelessaudeRS, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil
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Hartshorn JE, Nair RU. Dental innovations which will influence the oral health care of baby boomers. SPECIAL CARE IN DENTISTRY 2023; 43:359-369. [PMID: 36782274 DOI: 10.1111/scd.12835] [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: 11/15/2022] [Revised: 01/13/2023] [Accepted: 01/27/2023] [Indexed: 02/15/2023]
Abstract
From the widespread use of smartphones and tablets to the multitude of applications available, older adults are showing an interest in utilizing technology to maintain their independence and to improve their quality of life. As technology continues to advance and be incorporated into many day-to-day activities, the baby boom generation will see these changes affecting the way they access and utilize dental services. Innovative toothbrushes and chemotherapeutics are continuing to be developed and utilized by many older adults. Within the dental office, older adults are seeing greater application of technology in every day dental procedures. These include the use of teledentistry, artificial intelligence (AI), innovative restorative materials, digitization of fixed and removable prosthodontics, cone beam computed tomography (CBCT) scans to guide dental implant placement and endodontic procedures. There is also new technology to aid in cancer detection and shielding during cancer treatment. Improved communication between the medical and dental fields has become increasingly necessary to facilitate effective patient care and a few innovative healthcare systems have begun to consolidate these services. Overall, the baby boom generation will continue to see dental innovations that will change the way they experience everyday life and dental services.
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Affiliation(s)
- Jennifer E Hartshorn
- Department of Preventive and Community Dentistry, The University of Iowa College of Dentistry and Dental Clinics, Iowa City, Iowa, USA
| | - Rohit U Nair
- Department of Preventive and Community Dentistry, The University of Iowa College of Dentistry and Dental Clinics, Iowa City, Iowa, USA
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Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13030414. [PMID: 36766519 PMCID: PMC9913920 DOI: 10.3390/diagnostics13030414] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.
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Jayaram N, Muralidharan M, Muthupandian S. The use of multilayer perceptron and radial basis function: an artificial intelligence model to predict progression of oral cancer. Int J Surg 2023; 109:57-59. [PMID: 36799795 PMCID: PMC10389180 DOI: 10.1097/js9.0000000000000026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 02/18/2023]
Affiliation(s)
- Nivedita Jayaram
- Department of Computing Technologies, SRM Institute of Science and Technology
| | - Manjusha Muralidharan
- AMR and Nanomedicine Laboratory, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
| | - Saravanan Muthupandian
- AMR and Nanomedicine Laboratory, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
- Department of Medical Microbiology and Immunology, Institute of Biomedical Sciences, College of Health Sciences, Mekelle University, Mekelle, Ethiopia
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Dholariya S, Singh RD, Sonagra A, Yadav D, Vajaria BN, Parchwani D. Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis. Crit Rev Oncog 2023; 28:11-44. [PMID: 37830214 DOI: 10.1615/critrevoncog.2023047772] [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: 10/14/2023]
Abstract
Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.
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Affiliation(s)
- Sagar Dholariya
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Rajkot, Gujarat, India
| | - Ragini D Singh
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Rajkot, Gujarat, India
| | - Amit Sonagra
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Rajkot, Gujarat, India
| | | | | | - Deepak Parchwani
- Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Rajkot, Gujarat, India
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Muacevic A, Adler JR. Role of Advanced Diagnostic Aids in the Detection of Potentially Malignant Disorders and Oral Cancer at an Early Stage. Cureus 2023; 15:e34113. [PMID: 36843823 PMCID: PMC9949752 DOI: 10.7759/cureus.34113] [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: 11/02/2022] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
One of the most prevalent malignancies diagnosed today is cancer of the mouth or oral cancer. Compared to systemic malignancies like lung cancer, colon cancer, etc., oral cancer tends to get less attention from the general public. However, these lesions may be lethal if not treated, even if diagnosed early. Early diagnosis improves the prognosis for successful therapy. Delayed diagnosis is hypothesized to be a pivotal contributor to the dismal oral cancer survival rate over five years. The current standard of care for diagnosis and detection is based on clinical evaluation, the histological study of biopsy material, and genetic methods. There have been several advancements in the diagnostic technologies available to detect oral cancer at the initial phase. This study aims to dissect the cutting-edge methods for detecting oral cancer in its earliest stages.
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Affiliation(s)
- Alexander Muacevic
- Department of Oral Maxillofacial Surgery (OMFS) and Diagnosis Sciences, College of Dentistry, Riyadh Elm University, Riyadh, SAU
| | - John R Adler
- Department of Oral Maxillofacial Surgery (OMFS) and Diagnosis Sciences, College of Dentistry, Riyadh Elm University, Riyadh, SAU
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Prakash S, Balaji JN, Veeraraghavan VP, Mohan SK. Telehealth: Is It a Post-COVID Reality in Early Diagnosis of Oral Cancer? J Contemp Dent Pract 2022; 23:1181-1182. [PMID: 37125512 DOI: 10.5005/jp-journals-10024-3356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Affiliation(s)
- Sreenidhi Prakash
- Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu, India, Orcid: https://orcid.org/0000-0001-7740-3762
| | - Jyotsna Needamangalam Balaji
- Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu, India, Orcid: https://orcid.org/0000-0002-6719-9136
| | - Vishnu Priya Veeraraghavan
- Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India, Phone: +91 9841445599, e-mail: , Orcid: https://orcid.org/0000-0002-5071-9860
| | - Surapaneni Krishna Mohan
- Panimalar Medical College Hospital & Research Institute, Varadharajapuram, Poonamallee, Chennai, Tamil Nadu, India; SMAART Population Health Informatics Intervention Center (SMAART PHIC), Foundation of Healthcare Technologies Society, Panimalar Medical College Hospital and Research Institute, Varadharajapuram, Chennai, Tamil Nadu, India, Phone: +91 9789099989, e-mail: , Orcid: https://orcid.org/0000-0002-5204-5708
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Misra SR, Das R. Artificial intelligence in oral oncology: Overcoming the challenges in diagnosis! Oral Oncol 2022; 135:106187. [DOI: 10.1016/j.oraloncology.2022.106187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 09/24/2022] [Indexed: 11/22/2022]
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Mathew R, Palatinus S, Padala S, Alshehri A, Awadh W, Bhandi S, Thomas J, Patil S. Neural networks for classification of cervical vertebrae maturation: a systematic review. Angle Orthod 2022; 92:796-804. [PMID: 36069934 PMCID: PMC9598845 DOI: 10.2319/031022-210.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/01/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To assess the accuracy of identification and/or classification of the stage of cervical vertebrae maturity on lateral cephalograms by neural networks as compared with the ground truth determined by human observers. MATERIALS AND METHODS Search results from four electronic databases (PubMed [MEDLINE], Embase, Scopus, and Web of Science) were screened by two independent reviewers, and potentially relevant articles were chosen for full-text evaluation. Articles that fulfilled the inclusion criteria were selected for data extraction and methodologic assessment by the QUADAS-2 tool. RESULTS The search identified 425 articles across the databases, from which 8 were selected for inclusion. Most publications concerned the development of the models with different input features. Performance of the systems was evaluated against the classifications performed by human observers. The accuracy of the models on the test data ranged from 50% to more than 90%. There were concerns in all studies regarding the risk of bias in the index test and the reference standards. Studies that compared models with other algorithms in machine learning showed better results using neural networks. CONCLUSIONS Neural networks can detect and classify cervical vertebrae maturation stages on lateral cephalograms. However, further studies need to develop robust models using appropriate reference standards that can be generalized to external data.
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Hegde S, Ajila V, Zhu W, Zeng C. Review of the Use of Artificial Intelligence in Early Diagnosis and Prevention of Oral Cancer. Asia Pac J Oncol Nurs 2022; 9:100133. [PMID: 36389623 PMCID: PMC9664349 DOI: 10.1016/j.apjon.2022.100133] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/12/2022] [Indexed: 11/30/2022] Open
Abstract
The global occurrence of oral cancer (OC) has increased in recent years. OC that is diagnosed in its advanced stages results in morbidity and mortality. The use of technology may be beneficial for early detection and diagnosis and thus help the clinician with better patient management. The advent of artificial intelligence (AI) has the potential to improve OC screening. AI can precisely analyze an enormous dataset from various imaging modalities and provide assistance in the field of oncology. This review focused on the applications of AI in the early diagnosis and prevention of OC. A literature search was conducted in the PubMed and Scopus databases using the search terminology “oral cancer” and “artificial intelligence.” Further information regarding the topic was collected by scrutinizing the reference lists of selected articles. Based on the information obtained, this article reviews and discusses the applications and advantages of AI in OC screening, early diagnosis, disease prediction, treatment planning, and prognosis. Limitations and the future scope of AI in OC research are also highlighted.
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Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2363410. [PMID: 35909480 PMCID: PMC9334076 DOI: 10.1155/2022/2363410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/18/2022]
Abstract
PVL (proliferative verrucous leukoplakia) has distinct clinical characteristics. They have a proclivity for multifocality, a high recurrence rate after treatment, and malignant transformation, and they can progress to verrucous or squamous cell carcinoma. AI can aid in the diagnosis and prognosis of cancers and other diseases. Computational algorithms can spot tissue changes that a pathologist might overlook. This method is only used in a few studies to diagnose LB and PVL. To see if their cellular nuclei differed and if this cellular compartment could classify them, researchers used a computational system and a polynomial classifier to compare OLs and PVLs. 161 OL and 3 PVL specimens in the lab were grown, photographed, and used for training and computation. Exam orders revealed patients' sociodemographics and clinical pathologies. The nucleus was segmented using Mask R-CNN, and LB and PVL were classified using a polynomial classifier based on nucleus area, perimeter, eccentricity, orientation, solidity, entropies, and Moran Index (a measure of disorderliness). The majority of OL patients were male smokers; most PVL patients were female, with a third having malignant transformation. The neural network correctly identified cell nuclei 92.95% of the time. Except for solidity, 11 of the 13 nuclear characteristics compared between the PVL and the LB showed significant differences. The 97.6% under the curve of the polynomial classifier was used to classify the two lesions. These results demonstrate that computational methods can aid in diagnosing these two lesions.
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Can Artificial Intelligence (AI) assist in the diagnosis of oral mucosal lesions and/or oral cancer? Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:413-414. [PMID: 36054978 DOI: 10.1016/j.oooo.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/23/2022]
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Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach. J Pers Med 2022; 12:jpm12040614. [PMID: 35455730 PMCID: PMC9032985 DOI: 10.3390/jpm12040614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/09/2022] [Accepted: 04/09/2022] [Indexed: 02/05/2023] Open
Abstract
Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.
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Early Diagnosis and Diagnostic Delay in Oral Cancer. Cancers (Basel) 2022; 14:cancers14071758. [PMID: 35406530 PMCID: PMC8996863 DOI: 10.3390/cancers14071758] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 12/21/2022] Open
Abstract
Oral cancer is a very serious public health problem in many parts of the world, particularly in developing countries, where about two-thirds of incident cases occur [...].
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Linxweiler M, Körner S, Wemmert S, Rimbach H, Helfrich J, Linxweiler B, Takacs ZF, Solomayer EF, Wagner M, Morris LGT, Schick B, Kühn JP. Cytology-based Cancer Surgery of the Head and Neck (CyCaS-HN): a prospective, randomized, controlled clinical trial. Eur Arch Otorhinolaryngol 2022; 279:4505-4514. [PMID: 35305137 PMCID: PMC9363327 DOI: 10.1007/s00405-022-07333-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/28/2022] [Indexed: 11/29/2022]
Abstract
Purpose Liquid-based cytology (LBC) is routinely used in gynecology but is rarely applied in head and neck oncology though many suspicious lesions are easily accessible. While several studies have evaluated the potential use of LBC for early detection and molecular characterization of head and neck squamous cell carcinomas (HNSCCs), no study investigated its potential role in surgical management and therapy planning so far. Methods Twenty-five patients with cT1-2 squamous cell carcinomas of the oral cavity and oropharynx were prospectively enrolled in this study and were randomized to two treatment arms: in the control arm, a diagnostic panendoscopy with incisional biopsy was followed by a second operation with transoral tumor resection ± neck dissection and tracheostomy. In the intervention arm, patients underwent LBC diagnostics and in case of a positive result received one single operation with panendoscopy and incisional biopsy for confirmation of LBC result by rapid section histology followed by transoral tumor resection ± neck dissection and tracheostomy in the same session. Results Time between clinical diagnosis and definitive surgical treatment was significantly shorter in the intervention group compared with the control group (p < 0.0001). Additionally, time of hospitalization (p < 0.0001) and cumulative operation time (p = 0.062) were shorter in the intervention group. No significant differences in overall, progression-free, and disease-specific survival were observed. Conclusion Cytology-based cancer surgery is a promising therapeutic strategy that can potentially be considered for a well-defined group of early-stage HNSCC patients and help to avoid repetitive general anesthesia, shorten the diagnosis-to-treatment interval and spare operation as well as hospitalization time.
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Affiliation(s)
- Maximilian Linxweiler
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany.
| | - Sandrina Körner
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
| | - Silke Wemmert
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
| | - Hugo Rimbach
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
| | - Johanna Helfrich
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
| | - Barbara Linxweiler
- Department of Gynecology, Obstetrics and Reproductive Medicine, Saarland University Medical Center, Homburg, Germany
| | - Zoltan Ferenc Takacs
- Department of Gynecology, Obstetrics and Reproductive Medicine, Saarland University Medical Center, Homburg, Germany
| | - Erich Franz Solomayer
- Department of Gynecology, Obstetrics and Reproductive Medicine, Saarland University Medical Center, Homburg, Germany
| | - Mathias Wagner
- Department of General and Surgical Pathology, Saarland University Medical Center, Homburg, Germany
| | - Luc G T Morris
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.,Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Bernhard Schick
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
| | - Jan Philipp Kühn
- Department of Otorhinolaryngology, Head and Neck Surgery, Saarland University Medical Center, Kirrbergerstr. 100, building 6, 66421, Homburg, Germany
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Ameli N, Gibson MP, Khanna A, Howey M, Lai H. An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes. FRONTIERS IN DENTAL MEDICINE 2022. [DOI: 10.3389/fdmed.2022.833191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.
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Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. FRONTIERS IN ORAL HEALTH 2022; 2:794248. [PMID: 35088057 PMCID: PMC8786902 DOI: 10.3389/froh.2021.794248] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A. Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Nath S, Raveendran R, Perumbure S. Artificial Intelligence and Its Application in the Early Detection of Oral Cancers. CLINICAL CANCER INVESTIGATION JOURNAL 2022. [DOI: 10.51847/h7wa0uhoif] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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