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Hsu Y, Chou CY, Huang YC, Liu YC, Lin YL, Zhong ZP, Liao JK, Lee JC, Chen HY, Lee JJ, Chen SJ. Oral mucosal lesions triage via YOLOv7 models. J Formos Med Assoc 2024:S0929-6646(24)00313-9. [PMID: 39003230 DOI: 10.1016/j.jfma.2024.07.010] [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: 04/25/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 07/15/2024] Open
Abstract
BACKGROUND/PURPOSE The global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions. METHODS A dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage. RESULTS The YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification. CONCLUSION The study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.
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Affiliation(s)
- Yu Hsu
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Cheng-Ying Chou
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yu-Cheng Huang
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Chieh Liu
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yong-Long Lin
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Zi-Ping Zhong
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Jun-Kai Liao
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Jun-Ching Lee
- Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsin-Yu Chen
- Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan
| | - Jang-Jaer Lee
- Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan; Department of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan.
| | - Shyh-Jye Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Radiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
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Esmaeilyfard R, Bonyadifard H, Paknahad M. Dental Caries Detection and Classification in CBCT Images Using Deep Learning. Int Dent J 2024; 74:328-334. [PMID: 37940474 PMCID: PMC10988262 DOI: 10.1016/j.identj.2023.10.003] [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: 06/30/2023] [Revised: 09/24/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVES This study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images. METHODS The CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification. RESULTS The diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations. CONCLUSIONS This research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry. CLINICAL SIGNIFICANCE This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.
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Affiliation(s)
- Rasool Esmaeilyfard
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
| | - Haniyeh Bonyadifard
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
| | - Maryam Paknahad
- Oral, and Dental Disease Research Center, Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
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Rokhshad R, Mohammad-Rahimi H, Price JB, Shoorgashti R, Abbasiparashkouh Z, Esmaeili M, Sarfaraz B, Rokhshad A, Motamedian SR, Soltani P, Schwendicke F. Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis. Clin Oral Investig 2024; 28:88. [PMID: 38217733 DOI: 10.1007/s00784-023-05475-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.
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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
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Jeffery B Price
- Department of Oncology and Diagnostic Sciences, University of Maryland, School of Dentistry, Baltimore, Maryland 650 W Baltimore St, Baltimore, MD, 21201, USA
| | - Reyhaneh Shoorgashti
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | | | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Bita Sarfaraz
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Arad Rokhshad
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Salamat Blv, Isfahan Dental School, Isfahan, Iran
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Nepales, Italy
| | - 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, Charitépl. 1, 10117, Berlin, Germany
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Rokhshad R, Salehi SN, Yavari A, Shobeiri P, Esmaeili M, Manila N, Motamedian SR, Mohammad-Rahimi H. Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis. Oral Radiol 2024; 40:1-20. [PMID: 37855976 DOI: 10.1007/s11282-023-00715-5] [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: 05/09/2023] [Accepted: 09/23/2023] [Indexed: 10/20/2023]
Abstract
PURPOSE This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data. METHODS Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers. The risk of bias was rated with the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. For the meta-analysis diagnostic odds ratio (DOR) was calculated. Deeks' funnel plot was used to assess publication bias. MIDAS and Metandi packages were used to analyze diagnostic test accuracy in STATA. RESULTS From 1967 studies, 32 were found eligible after the search and screening procedures. According to the QUADAS-2 tool, 7 included studies had a low risk of bias for all domains. According to the results of all included studies, the accuracy varied from 82.6 to 100%. Additionally, specificity ranged from 66.6 to 90.1%, sensitivity from 74 to 99.68%. Fourteen studies that provided sufficient data were included for meta-analysis. The pooled sensitivity was 90% (95% CI 0.820.94), and the pooled specificity was 92% (CI 95% 0.87-0.96). The DORs were 103 (27-251). Publication bias was not detected based on the p-value of 0.75 in the meta-analysis. CONCLUSION With a head and neck screening deep learning model, detectable screening processes can be enhanced with high specificity and sensitivity.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
| | - Seyyede Niloufar Salehi
- Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran
| | - Amirmohammad Yavari
- Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Nisha Manila
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
- Department of Diagnostic Sciences, Louisiana State University Health Science Center School of Dentistry, Louisiana, USA
| | - Saeed Reza Motamedian
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany.
- Dentofacial Deformities Research Center, Research Institute of Dental, Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjou Blvd, Tehran, Iran.
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany
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Badawy M, Balaha HM, Maklad AS, Almars AM, Elhosseini MA. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics (Basel) 2023; 8:499. [PMID: 37887629 PMCID: PMC10604828 DOI: 10.3390/biomimetics8060499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
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Affiliation(s)
- Mahmoud Badawy
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40208, USA
| | - Ahmed S. Maklad
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suif 62521, Egypt
| | - Abdulqader M. Almars
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
| | - Mostafa A. Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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Tariq A, Nakhi FB, Salah F, Eltayeb G, Abdulla GJ, Najim N, Khedr SA, Elkerdasy S, Al-Rawi N, Alkawas S, Mohammed M, Shetty SR. Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review. Imaging Sci Dent 2023; 53:193-198. [PMID: 37799746 PMCID: PMC10548158 DOI: 10.5624/isd.20230092] [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: 04/26/2023] [Revised: 06/18/2023] [Accepted: 06/23/2023] [Indexed: 10/07/2023] Open
Abstract
Purpose Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.
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Affiliation(s)
- Asmhan Tariq
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Fatmah Bin Nakhi
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Fatema Salah
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Gabass Eltayeb
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Ghada Jassem Abdulla
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Noor Najim
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Salma Ahmed Khedr
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Sara Elkerdasy
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Natheer Al-Rawi
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Sausan Alkawas
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Marwan Mohammed
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Shishir Ram Shetty
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
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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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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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10
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Wolk R, Lingen MW. Proceedings of the North American Society of Head and Neck Pathology Companion Meeting, New Orleans, LA, March 12, 2023: Oral Cavity Dysplasia: Why Does Histologic Grading Continue to be Contentious? Head Neck Pathol 2023; 17:292-298. [PMID: 37184731 PMCID: PMC10293486 DOI: 10.1007/s12105-023-01544-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 03/01/2023] [Indexed: 05/16/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) is the world's 6th most common malignancy. Oral cavity SCC (OCSCC) represents approximately one third of the HNSCC cases diagnosed annually in the United States. Despite therapeutic advances, OCSCC is frequently lethal, with a modest 5-year survival. Because OCSCC is often preceded by premalignant lesions, it is an ideal disease for screening initiatives. The conventional visual and tactile exam (CVTE), coupled with a tissue biopsy, remains the gold standard. However, CVTE alone cannot reliably differentiate between reactive/inflammatory and dysplastic lesions. Further, the histologic diagnosis of dysplasia is subjective in nature and a highly imperfect predictor of malignant transformation. This prognostic uncertainty creates a significant clinical management dilemma-watchful waiting with increased patient psychological and economic burdens versus unnecessary aggressive treatment. As such, the development and validation of novel diagnostic platforms such as Artificial Intelligence (AI) and prognostic molecular biomarkers may help address these critical unmet clinical needs.
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Affiliation(s)
- Rachelle Wolk
- Department of Pathology, University of Chicago Medicine, 5841 South Maryland Avenue, MC 6101, Chicago, IL, 60637, USA
| | - Mark W Lingen
- Department of Pathology, University of Chicago Medicine, 5841 South Maryland Avenue, MC 6101, Chicago, IL, 60637, USA.
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11
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Jain S, Naicker D, Raj R, Patel V, Hu YC, Srinivasan K, Jen CP. Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials. Diagnostics (Basel) 2023; 13:diagnostics13091563. [PMID: 37174954 PMCID: PMC10178016 DOI: 10.3390/diagnostics13091563] [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: 03/04/2023] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.
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Affiliation(s)
- Somit Jain
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Dharmik Naicker
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Ritu Raj
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Vedanshu Patel
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Chun-Ping Jen
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Mechanical Engineering and Advanced Institute of Manufacturing for High-Tech Innovations, National Chung Cheng University, Chia-Yi 62102, Taiwan
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12
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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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13
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Issa J, Jaber M, Rifai I, Mozdziak P, Kempisty B, Dyszkiewicz-Konwińska M. Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040768. [PMID: 37109726 PMCID: PMC10142688 DOI: 10.3390/medicina59040768] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023]
Abstract
This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.
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Affiliation(s)
- Julien Issa
- Department of Diagnostics, University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland
- Doctoral School, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland
| | - Mouna Jaber
- Faculty of Dentistry, Poznan University of Medical Sciences, 60-812 Poznan, Poland
| | - Ismail Rifai
- Department of Restorative Dentistry and Endodontics, Universitat Internacional de Catalunya, Josep Trueta, s/n, 08195 Sant Cugat del Vallès, Spain
| | - Paul Mozdziak
- Prestage Department of Poultry Sciences, North Carolina State University, Raleigh, NC 27695, USA
- Physiology Graduate Faculty, North Carolina State University, Raleigh, NC 27695, USA
| | - Bartosz Kempisty
- Physiology Graduate Faculty, North Carolina State University, Raleigh, NC 27695, USA
- Division of Anatomy, Department of Human Morphology and Embryology, Wroclaw Medical University, Chalubinskiego 6a, 50-368 Wroclaw, Poland
- Department of Veterinary Surgery, Institute of Veterinary Medicine, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
- Center of Assisted Reproduction, Department of Obstetrics and Gynaecology, University Hospital and Masaryk University, Jihlavska 20, 62500 Brno, Czech Republic
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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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15
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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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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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