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Lam V, O'Brien O, Amin O, Nigar E, Kumar M, Lingam RK. Oral cavity cancer and its pre-treatment radiological evaluation: A pictorial overview. Eur J Radiol 2024; 176:111494. [PMID: 38776803 DOI: 10.1016/j.ejrad.2024.111494] [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: 04/12/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE Oral cavity cancer, primarily squamous cell carcinoma (SCC), is a prevalent malignancy globally, necessitating accurate clinical assessment and staging to enable effective treatment planning. Diagnosis requires biopsy and is followed by surgical resection and reconstruction as the primary therapeutic modality. Imaging plays a pivotal role during this process, aiding in the evaluation of tumour extent, nodal involvement and distant metastases. However, despite its value, both radiologists and clinicians must recognise its inherent limitations. METHODS This pictorial review article aims to illustrate the application of various imaging modalities in the pre-treatment evaluation of oral cavity SCC and highlights potential pitfalls. It underscores the importance of understanding the anatomical subsites of the oral cavity, the diverse patterns of spread tumours exhibit at each site, alongside the role of imaging in facilitating informed management strategies, while also acknowledging its limitations. RESULTS The review delves into fundamentals of current staging including nodal involvement, while, emphasising imaging strategies and potential limitations. Finally, it touches on the potential of novel radiomic techniques in characterising tumours and predicting treatment response. CONCLUSIONS Pre-treatment oral cavity cancer staging reflects an ongoing quest for enhanced diagnostic accuracy and prognostic prediction. Recognising the value of imaging alongside its limitations fosters a multidisciplinary approach to treatment planning, ultimately improving patient outcomes.
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Affiliation(s)
- Vincent Lam
- Department of Radiology, Leicester Royal Infirmary, University Hospitals of Leicester NHS Trust, Infirmary Square, Leicester LE1 5WW, United Kingdom
| | - Owen O'Brien
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom
| | - Omed Amin
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom; Department of Radiology, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Rd, London SW10 9NH, United Kingdom
| | - Ezra Nigar
- Department of Pathology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom
| | - Mahesh Kumar
- Department of Oral and Maxillofacial Surgery, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom; Department of Oral and Maxillofacial Surgery, Hillingdon Hospital, The Hillingdon Hospitals NHS Foundation Trust, Pield Heath Rd, Uxbridge UB8 3NN, United Kingdom
| | - Ravi Kumar Lingam
- Department of Radiology, Northwick Park Hospital, London North West University Healthcare NHS Trust, Watford Road, London HA1 3UJ, United Kingdom.
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Jain DA, Gupta N, Sharma DP, Gupta DOP, Gupta DS, Sahoo DAK. WITHDRAWN: Histomorphometric Image Classifier of Different Grades of Oral Squamous Cell Carcinoma Using Transfer Learning and Convolutional Neural Network. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101876. [PMID: 38636805 DOI: 10.1016/j.jormas.2024.101876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024]
Abstract
This article has been withdrawn at the request of the authors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal
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Affiliation(s)
- Dr Ayushi Jain
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Nitika Gupta
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Dr Pooja Sharma
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India
| | - Dr Om Prakash Gupta
- Department of General Surgery, Career Institute of Medical sciences, Lucknow 226003, UP, India
| | - Dr Shalini Gupta
- Department of Oral Pathology, King George's Medical University, Lucknow 226003, UP, India.
| | - Dr Amaresh Kumar Sahoo
- Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj 211012, UP, India.
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3
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Menditti D, Santagata M, Guida D, Magliulo R, D'Antonio GM, Staglianò S, Boschetti CE. State of the Art in the Diagnosis and Assessment of Oral Malignant and Potentially Malignant Disorders: Present Insights and Future Outlook-An Overview. Bioengineering (Basel) 2024; 11:228. [PMID: 38534502 DOI: 10.3390/bioengineering11030228] [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: 01/25/2024] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024] Open
Abstract
Oral Potentially Malignant Disorder (OPMD) is a significant concern for clinicians due to the risk of malignant transformation. Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer with a low survival rate, causing over 200,000 new cases globally each year. Despite advancements in diagnosis and treatment, the five-year survival rate for OSCC patients remains under 50%. Early diagnosis can greatly improve the chances of survival. Therefore, understanding the development and transformation of OSCC and developing new diagnostic methods is crucial. The field of oral medicine has been advanced by technological and molecular innovations, leading to the integration of new medical technologies into dental practice. This study aims to outline the potential role of non-invasive imaging techniques and molecular signatures for the early detection of Oral Malignant and Potentially Malignant Disorders.
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Affiliation(s)
- Dardo Menditti
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Mario Santagata
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - David Guida
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Roberta Magliulo
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Giovanni Maria D'Antonio
- Division of Plastic and Reconstructive Surgery, Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90128 Palermo, Italy
| | - Samuel Staglianò
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Ciro Emiliano Boschetti
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
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Lee SJ, Oh HJ, Son YD, Kim JH, Kwon IJ, Kim B, Lee JH, Kim HK. Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning. BMC Oral Health 2024; 24:161. [PMID: 38302981 PMCID: PMC10832072 DOI: 10.1186/s12903-024-03898-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset. METHODS A total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network. RESULTS Utilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative [Formula: see text] score improvement rate of OPMD was 4.9%. CONCLUSIONS The present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.
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Affiliation(s)
- Sung-Jae Lee
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam, Republic of Korea
| | - Hyun Jun Oh
- Oral Oncology Clinic, National Cancer Center, Goyang, Republic of Korea
| | - Young-Don Son
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam, Republic of Korea
- Neuroscience Research Institute, Gachon Advanced Institute for Health Science and Technology, Gachon University, Incheon, Republic of Korea
| | - Jong-Hoon Kim
- Neuroscience Research Institute, Gachon Advanced Institute for Health Science and Technology, Gachon University, Incheon, Republic of Korea
- Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea
| | - Ik-Jae Kwon
- Department of Oral and Maxillofacial Surgery, Seoul National University Dental Hospital, Seoul, Republic of Korea
- Dental Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Bongju Kim
- Dental Life Science Research Institute, Seoul National University Dental Hospital, Seoul, Republic of Korea
| | - Jong-Ho Lee
- Oral Oncology Clinic, National Cancer Center, Goyang, Republic of Korea.
- Dental Life Science Research Institute, Seoul National University Dental Hospital, Seoul, Republic of Korea.
| | - Hang-Keun Kim
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam, Republic of Korea.
- Neuroscience Research Institute, Gachon Advanced Institute for Health Science and Technology, Gachon University, Incheon, Republic of Korea.
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Chen XY, Zhou G, Zhang J. Optical coherence tomography: Promising imaging technique for the diagnosis of oral mucosal diseases. Oral Dis 2024. [PMID: 38191786 DOI: 10.1111/odi.14851] [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: 05/04/2023] [Revised: 11/02/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE This review aims to summarize the latest application of optical coherence tomography (OCT) in oral mucosal diseases, promoting an accurate and earlier diagnosis of such disorders, which are difficult to be differentiated. SUBJECTIVE AND METHODS References on the application of OCT in oral mucosal diseases were mainly obtained from PubMed, Embase, Web of Science and Scopus databases, using the keywords: "optical coherence tomography and 'oral mucosa/oral cancers/oral potentially malignant diseases/oral lichen planus/oral leukoplakia/oral erythroplakia/discoid lupus erythematosus/oral autoimmune bullous diseases/oral ulcers/erythema multiforme/oral mucositis'". RESULTS It is found that OCT is showing a promising application potential in the early detection, diagnosis, differential diagnosis, monitoring of oral cancer and oral dysplastic lesions, as well as the delineation of tumor margins. OCT is also playing an increasingly important role in the diagnosis of oral potentially malignant disorders, oral mucosal bullous diseases, oral ulcerative diseases, erythema multiforme, and the early detection of oral mucositis. CONCLUSION Optical coherence tomography, as a novel optical technique featured by real-time, noninvasive, dynamic and high-resolution imaging, is of great use to serve as an adjunct tool for the diagnosis, differential diagnosis, monitoring and therapy evaluation of oral mucosal diseases.
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Affiliation(s)
- Xu-Ya Chen
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Gang Zhou
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jing Zhang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China
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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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7
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Farajollahi M, Safarian MS, Hatami M, Esmaeil Nejad A, Peters OA. Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review. AUST ENDOD J 2023; 49:720-734. [PMID: 37439465 DOI: 10.1111/aej.12775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.
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Affiliation(s)
- Mehran Farajollahi
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Sadegh Safarian
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Masoud Hatami
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Esmaeil Nejad
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ove A Peters
- School of Dentistry, The University of Queensland, Herston, Queensland, Australia
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8
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Giraldo-Roldan D, Ribeiro ECC, Araújo ALD, Penafort PVM, Silva VMD, Câmara J, Pontes HAR, Martins MD, Oliveira MC, Santos-Silva AR, Lopes MA, Kowalski LP, Moraes MC, Vargas PA. Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas. J Oral Pathol Med 2023; 52:988-995. [PMID: 37712132 DOI: 10.1111/jop.13481] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/08/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility. METHODS Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma). RESULTS The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1-score, respectively. CONCLUSIONS The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.
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Affiliation(s)
- Daniela Giraldo-Roldan
- Department of Oral Diagnosis, Piracicaba Dental School, State University of Campinas, Piracicaba, Brazil
| | | | | | | | - Viviane Mariano da Silva
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, Brazil
| | - Jeconias Câmara
- Department of Pathology and Legal Medicine, School of Medicine, Federal University of Amazon, Manaus, Brazil
| | | | - Manoela Domingues Martins
- Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Márcio Campos Oliveira
- Department of Health, State University of Feira de Santana (UEFS), Feira de Santana, Brazil
| | - Alan Roger Santos-Silva
- Department of Oral Diagnosis, Piracicaba Dental School, State University of Campinas, Piracicaba, Brazil
| | - Marcio Ajudarte Lopes
- Department of Oral Diagnosis, Piracicaba Dental School, State University of Campinas, Piracicaba, Brazil
| | - Luiz Paulo Kowalski
- Head and Neck Surgery Department, University of São Paulo Medical School, São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, Brazil
| | - Pablo Agustin Vargas
- Department of Oral Diagnosis, Piracicaba Dental School, State University of Campinas, Piracicaba, Brazil
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Borkar S, Reche A, Paul P, Deshpande A, Deshpande M. Noninvasive Technique for the Screening and Diagnosis of Oral Squamous Cell Carcinoma. Cureus 2023; 15:e46300. [PMID: 37915878 PMCID: PMC10616636 DOI: 10.7759/cureus.46300] [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: 08/23/2023] [Accepted: 09/30/2023] [Indexed: 11/03/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancy. Squamous cell carcinoma is the second-most prevalent type of cutaneous malignancy after basal cell carcinoma. Biopsy followed by histopathological assessment is the primary basis for assessing squamous cell carcinoma, but nowadays optical non-invasive screening modalities are gaining more importance. There has been an emphasis on implementing relatively quick, affordable, and non-invasive screening methodologies because of various limitations associated with conventional screening techniques, including the encroaching characteristic of the biopsy technique, and the increased price value for treatment. Liquid biopsy, optical detection systems, oral brush cytology, and microfluidic detection, are a few examples of these, each of which has advantages and disadvantages of their own. Dermoscopy is one of the fundamental non-invasive screening techniques used for the examination of cutaneous lesions in clinical practice. Optical coherence tomography and high-frequency ultrasound are considered to be beneficial, particularly for assessing the dimensions of tumors before surgery. The primary site of the lesions, tumor diameter, and the state of the operative borders are some factors that can influence prognosis.
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Affiliation(s)
- Shreyash Borkar
- Public Health 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
| | - Priyanka Paul
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anvika Deshpande
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Mihika Deshpande
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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10
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Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. THE JOURNAL OF FORENSIC ODONTO-STOMATOLOGY 2023; 41:30-41. [PMID: 37634174 PMCID: PMC10473456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
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Affiliation(s)
- M Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| | - M Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - D P Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - I Galić
- School of Medicine, University of Split, Croatia
| | - H Brkić
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
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11
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Huang H, Zheng O, Wang D, Yin J, Wang Z, Ding S, Yin H, Xu C, Yang R, Zheng Q, Shi B. ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. Int J Oral Sci 2023; 15:29. [PMID: 37507396 PMCID: PMC10382494 DOI: 10.1038/s41368-023-00239-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
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Affiliation(s)
- Hanyao Huang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| | - Ou Zheng
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA.
| | - Dongdong Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Jiayi Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zijin Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Shengxuan Ding
- College of Transportation Engineering, University of Central Florida, Orlando, USA
| | - Heng Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chuan Xu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
- C2SMART Center, Tandon School of Engineering, New York University, Brooklyn, USA
| | - Renjie Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qian Zheng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bing Shi
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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12
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Optimal deep learning neural network using ISSA for diagnosing the oral cancer. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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13
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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14
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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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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: 6] [Impact Index Per Article: 6.0] [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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16
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Mahajan A, Agarwal U, PG N, Vaish R, Shukla S, Sahu A, Bhalla AS, Patil V, Ankathi SK, Laskar SG, Patil V, Noronha V, Menon N, Prabhash K, Shah D, Patil A, Ahuja A, Chaturvedi P, Pai PS, Dcruz AK. Imaging Recommendations for Diagnosis, Staging, and Management of Oral Cancer. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1760314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
AbstractOral cavity cancers contribute to a majority of cancers in India. Clinical examination alone cannot determine the deeper extent of the disease; therefore, need for cross-sectional imaging including computed tomography and magnetic resonance imaging becomes indispensable for pre-treatment evaluation to decide optimal plan of management. Oral cavity squamous cell cancers (OSCC) can be treated with surgery alone, whereas deep muscle, neurovascular, osseous, or nodal involvement on imaging suggests advanced disease that requires a combination of surgery, radiation, and/or chemotherapy. Because of the complex anatomy of the oral cavity and its surrounding structures, imaging is crucial for locoregional staging and early detection of distant metastases. Imaging plays indispensable role not only in diagnosis but also in planning the management. An optimal guideline paper for developing countries like India is lacking that not only helps standardize the management but will also assist oncologists make reasonable decisions and reduce the unnecessary imaging. This imaging guideline paper will discuss the optimal imaging in diagnosis and management OSCC for Indian subcontinent.
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Affiliation(s)
- Abhishek Mahajan
- Department of Radiodiagnosis, The Clatterbridge Cancer Centre, Liverpool, United Kingdom
| | - Ujjwal Agarwal
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Nandakumar PG
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Richa Vaish
- Department of Head and Neck Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Shreya Shukla
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Arpita Sahu
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Parel, Mumbai, Maharashtra, India
| | - Ashu Seith Bhalla
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Vasundhara Patil
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Parel, Mumbai, Maharashtra, India
| | - Suman Kumar Ankathi
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Parel, Mumbai, Maharashtra, India
| | - Sarbani Ghosh Laskar
- Department of Radiation Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Vijay Patil
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Vanita Noronha
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Nandini Menon
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Diva Shah
- Department of Radiodiagnosis, HCG Cancer Centre, Vadodara, Gujarat, India
| | - Asawari Patil
- Department of Pathology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Ankita Ahuja
- Department of Radiodiagnosis, Innovision Imaging, Mumbai, Maharashtra, India
| | - Pankaj Chaturvedi
- Department of Head and Neck Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Prathamesh S. Pai
- Department of Head and Neck Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - A K Dcruz
- Apollo Hospitals, Belapur, Mumbai, India
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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Wang S, Yang M, Li R, Bai J. Current advances in noninvasive methods for the diagnosis of oral squamous cell carcinoma: a review. Eur J Med Res 2023; 28:53. [PMID: 36707844 PMCID: PMC9880940 DOI: 10.1186/s40001-022-00916-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/28/2022] [Indexed: 01/28/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC), one of the most common types of cancers worldwide, is diagnosed mainly through tissue biopsy. However, owing to the tumor heterogeneity and other drawbacks, such as the invasiveness of the biopsy procedure and high cost and limited usefulness of longitudinal surveillance, there has been a focus on adopting more rapid, economical, and noninvasive screening methods. Examples of these include liquid biopsy, optical detection systems, oral brush cytology, microfluidic detection, and artificial intelligence auxiliary diagnosis, which have their own strengths and weaknesses. Extensive research is being performed on various liquid biopsy biomarkers, including novel microbiome components, noncoding RNAs, extracellular vesicles, and circulating tumor DNA. The majority of these elements have demonstrated encouraging clinical outcomes in early OSCC detection. This review summarizes the screening methods for OSCC with a focus on providing new guiding strategies for the diagnosis of the disease.
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Affiliation(s)
- Shan Wang
- grid.443397.e0000 0004 0368 7493Department of Oral Pathology, School of Stomatology, Hainan Medical College, Haikou, 571199 People’s Republic of China ,grid.443397.e0000 0004 0368 7493Department of Stomatology, The Second Affiliated Hospital of Hainan Medical University, Haikou, 570216 People’s Republic of China
| | - Mao Yang
- grid.13291.380000 0001 0807 1581West China School of Stomatology, Sichuan University, Chengdu, 610041 People’s Republic of China
| | - Ruiying Li
- grid.443397.e0000 0004 0368 7493Department of Oral Pathology, School of Stomatology, Hainan Medical College, Haikou, 571199 People’s Republic of China
| | - Jie Bai
- grid.13402.340000 0004 1759 700XDepartment of Ophthalmology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000 People’s Republic of China
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Dubuc A, Zitouni A, Thomas C, Kémoun P, Cousty S, Monsarrat P, Laurencin S. Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study. J Clin Med 2022; 11:jcm11216596. [PMID: 36362822 PMCID: PMC9654969 DOI: 10.3390/jcm11216596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.
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Affiliation(s)
- Antoine Dubuc
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
| | - Anissa Zitouni
- Oral Surgery and Oral Medicine Department, CHU Limoges, 87000 Limoges, France
| | - Charlotte Thomas
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- InCOMM, I2MC, UMR 1297, Paul Sabatier University, 31062 Toulouse, France
| | - Philippe Kémoun
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
| | - Sarah Cousty
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- LAPLACE, UMR 5213 CNRS, Paul Sabatier University, 31062 Toulouse, France
| | - Paul Monsarrat
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
| | - Sara Laurencin
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
- Correspondence:
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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Bansal K, Bathla RK, Kumar Y. Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft comput 2022. [DOI: 10.1007/s00500-022-07246-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Ezzibdeh R, Munjal T, Ahmad I, Valdez TA. Artificial intelligence and tele-otoscopy: A window into the future of pediatric otology. Int J Pediatr Otorhinolaryngol 2022; 160:111229. [PMID: 35816971 DOI: 10.1016/j.ijporl.2022.111229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022]
Abstract
Telehealth in otolaryngology is gaining popularity as a potential tool for increased access for rural populations, decreased specialist wait times, and overall savings to the healthcare system. The adoption of telehealth has been dramatically increased by the COVID-19 pandemic limiting patients' physical access to hospitals and clinics. One of the key challenges to telehealth in general otolaryngology and otology specifically is the limited physical examination possible on the ear canal and middle ear. This is compounded in pediatric populations who commonly present with middle ear pathologies which can be challenging to diagnose even in the clinic. To address this need, various otoscopes have been designed to allow patients, their parents, or primary care providers to image the tympanic membrane and middle ear, and send data to otolaryngologists for review. Furthermore, the ability of these devices to capture images in digital format has opened the possibility of using artificial intelligence for quick and reliable diagnostic workup. In this manuscript, we provide a concise review of the literature regarding the efficacy of remote otoscopy, as well as recent efforts on the use of artificial intelligence in aiding otologic diagnoses.
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Affiliation(s)
- Rami Ezzibdeh
- Department of Otolaryngology Head and Neck Surgery, Stanford University School of Medicine, United States.
| | - Tina Munjal
- Department of Otolaryngology Head and Neck Surgery, Stanford University School of Medicine, United States.
| | - Iram Ahmad
- Department of Otolaryngology Head and Neck Surgery, Stanford University School of Medicine, United States.
| | - Tulio A Valdez
- Department of Otolaryngology Head and Neck Surgery, Stanford University School of Medicine, United States.
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23
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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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Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning. SENSORS 2022; 22:s22103833. [PMID: 35632242 PMCID: PMC9146317 DOI: 10.3390/s22103833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/06/2022] [Accepted: 05/17/2022] [Indexed: 02/06/2023]
Abstract
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
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Al-Rawi N, Sultan A, Rajai B, Shuaeeb H, Alnajjar M, Alketbi M, Mohammad Y, Shetty SR, Mashrah MA. The Effectiveness of Artificial Intelligence in Detection of Oral Cancer. Int Dent J 2022; 72:436-447. [PMID: 35581039 PMCID: PMC9381387 DOI: 10.1016/j.identj.2022.03.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 02/07/2023] Open
Abstract
Aim The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. Materials and methods From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. Results Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. Conclusions There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.
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Affiliation(s)
- Natheer Al-Rawi
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Afrah Sultan
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Batool Rajai
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Haneen Shuaeeb
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Mariam Alnajjar
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Maryam Alketbi
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Yara Mohammad
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates
| | - Shishir Ram Shetty
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates.
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Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images. Radiol Res Pract 2022; 2022:1614838. [PMID: 35502299 PMCID: PMC9056242 DOI: 10.1155/2022/1614838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/23/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.
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Yang SY, Li SH, Liu JL, Sun XQ, Cen YY, Ren RY, Ying SC, Chen Y, Zhao ZH, Liao W. Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning. J Dent Res 2022; 101:1321-1327. [PMID: 35446176 DOI: 10.1177/00220345221089858] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.
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Affiliation(s)
- S Y Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - S H Li
- National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - J L Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X Q Sun
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Cen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - R Y Ren
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - S C Ying
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Y Chen
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Z H Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - W Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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Vats R, Rai R, Kumar M. Detecting Oral Cancer: The Potential of Artificial Intelligence. Curr Med Imaging 2022; 18:919-923. [PMID: 35400347 DOI: 10.2174/1573405618666220408103549] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/05/2022] [Accepted: 01/31/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Physical inspection is a simple way to diagnose oral cancer. Most cases of oral cancer, on the contrary, are diagnosed late, resulting in needless mortality and morbidity. While screening high-risk populations appear to be helpful, these people are often found in areas with minimal access to health care. In this paper, we have reviewed several aspects related to oral cancer such as its cause, the risk factors associated with it, India's oral cancer situation at the moment, various screening methods, and the ability of artificial intelligence in the detection and classification purpose. Oral cancer results can be enhanced by combining imaging and artificial intelligence approaches for better detection and diagnosis. OBJECTIVE This paper aims to cover the various oral cancer screening detection techniques that use Artificial Intelligence (AI). METHODS In this paper, we have covered the imaging methods that are used in screening oral cancer and after that the potential of AI for the detection of oral cancer. CONCLUSION This paper covers some of the main concepts regarding oral cancer and various AI methods used to detect it.
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Affiliation(s)
- Rishabh Vats
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Manoj Kumar
- Department of Computer Engineering and Applications, GLA University, Mathura, India
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Sun TG, Mao L, Chai ZK, Shen XM, Sun ZJ. Predicting the Proliferation of Tongue Cancer With Artificial Intelligence in Contrast-Enhanced CT. Front Oncol 2022; 12:841262. [PMID: 35463386 PMCID: PMC9026338 DOI: 10.3389/fonc.2022.841262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with the Ki-67 index has great impact on tumor microenvironment, therapeutic strategy making, and patients’ prognosis. However, the most commonly used method to obtain the proliferation status is through biopsy or surgical immunohistochemical staining. Noninvasive method before operation remains a challenge. Hence, in this study, we aimed to validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on artificial intelligence (AI). CECT images of the lesion area from 179 TSCC patients were analyzed using a convolutional neural network (CNN). Patients were divided into a high proliferation status group and a low proliferation status group according to the Ki-67 index of patients with the median 20% as cutoff. The model was trained and then the test set was automatically classified. Results of the test set showed an accuracy of 65.38% and an AUC of 0.7172, suggesting that the majority of samples were classified correctly and the model was stable. Our study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation.
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Affiliation(s)
- Ting-Guan Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Liang Mao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zi-Kang Chai
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xue-Meng Shen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhi-Jun Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
- *Correspondence: Zhi-Jun Sun,
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Baniulyte G, Ali K. Artificial intelligence - can it be used to outsmart oral cancer? Evid Based Dent 2022; 23:12-13. [PMID: 35338317 DOI: 10.1038/s41432-022-0238-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 11/10/2022]
Abstract
Data Sources Electronic search on PubMed, Cochrane, Scopus, Embase, Google Scholar, Saudi Digital Library and Web of Science, and hand searching carried out for studies published January 2000-March 2021. Language was restricted to English.Study selection Original research studies involving artificial intelligence technology for oral cancer diagnosis and prognosis prediction were considered. The studies had to provide quantitative data of their evaluation analysis. The exclusion criteria were reported. No limit was set on study design.Data extraction and synthesis The initial search yielded 628 articles. Following deduplication, 340 full-text articles were screened. QUADAS-2 tool was used to assess the quality of the included studies regarding diagnostic accuracy.Results A total of 16 studies were included with various study designs: 14 cross-sectional, one cohort and one retrospective study. Six studies reviewed the diagnosis aspect. All studies indicate an overall positive trend of artificial intelligence technology.Conclusions Artificial intelligence appears to have good accuracy in oral cancer diagnosis and its prediction.
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Affiliation(s)
- G Baniulyte
- Academic Clinical Fellow in Oral Surgery, Oral and Maxillofacial Department, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - K Ali
- Qatar University, College of Dental Medicine, QU Health, Doha 2713, Qatar
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Reid J, Parmar P, Lund T, Aalto DK, Jeffery CC. Development of a machine-learning based voice disorder screening tool. Am J Otolaryngol 2022; 43:103327. [PMID: 34923280 DOI: 10.1016/j.amjoto.2021.103327] [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: 10/29/2021] [Accepted: 12/08/2021] [Indexed: 01/20/2023]
Abstract
OBJECTIVE Early recognition and referral are crucial for voice disorder management. Limited availability of subspecialists, poor primary care awareness, and the need for specialized equipment impede effective care. Thus, there is a need for a tool to improve voice pathology screening. Machine learning algorithms (MLAs) have shown promise in analyzing acoustic characteristics of phonation. However, few studies report clinical applications of MLAs for voice pathology detection. The objective of this study was to design and validate a MLA for detecting pathological voices. METHODS A MLA was developed for voice analysis. Audio samples converted into spectrograms were inputted into a pre-existing VGG19 convolutional neural network (CNN) and image-classifier. The resulting feature map was classified as either pathological or healthy using a Support Vector Machine (SVM) binary linear classifier. This combined MLA was "trained" with 950 sustained "/i/" vowel audio samples from the Saarbrucken Voice Database (SVD), which contains subjects with and without voice disorders. The trained MLA was "tested" with 406 SVD samples to determine sensitivity, specificity, and overall accuracy. External validation of the MLA was performed using clinical voice samples collected from patients attending a subspecialty voice clinic. RESULTS The MLA detected pathologies in SVD samples with 98.5% sensitivity, 97.1% specificity and 97.8% overall accuracy. In 30 samples obtained prospectively from voice clinic patients, the MLA detected pathologies with 100% sensitivity, 96.3% specificity and 96.7% overall accuracy. CONCLUSIONS This study demonstrates that a MLA using a simple audio input can detect diverse vocal pathologies with high sensitivity and specificity. Thus, this algorithm shows promise as a potential screening tool.
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Affiliation(s)
- Jonathan Reid
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Preet Parmar
- Department of Physics, Faculty of Science, University of Alberta, Edmonton, AB, Canada
| | - Tyler Lund
- Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | - Daniel K Aalto
- Communication Sciences and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Caroline C Jeffery
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada; Communication Sciences and Disorders, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada.
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Xu Y, Deng X, Sun Y, Wang X, Xiao Y, Li Y, Chen Q, Jiang L. Optical Imaging in the Diagnosis of OPMDs Malignant Transformation. J Dent Res 2022; 101:749-758. [PMID: 35114846 DOI: 10.1177/00220345211072477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Oral potentially malignant disorders (OPMDs) are a heterogeneous group of oral lesions with a variable risk of malignant transformation to oral squamous cell carcinoma. The current OPMDs malignant transformation screening depends on conventional oral examination (COE) and is confirmed by biopsy and histologic examination. However, early malignant lesions with subtle mucosal changes are easily unnoticed by COE based on visual inspection and palpation. Optical techniques have been used to determine the biological structure, composition, and function of cells and tissues noninvasively by analyzing the changes in their optical properties. The oral epithelium and stroma undergo persistent structural, functional, and biochemical alterations during malignant transformation, leading to variations in optical tissue properties; optical techniques are thus powerful tools for detecting OPMDs malignant transformation. The optical imaging methods already used to detect OPMDs malignant transformation in vivo include autofluorescence imaging, narrowband imaging, confocal reflectance microscopy, and optical coherence tomography. They exhibit advantages over COE in detecting biochemical or morphologic changes at the molecular or cellular level in vivo; however, limitations also exist. This article comprehensively reviews the various real-time in vivo optical imaging methods used in the adjunctive diagnosis of OPMDs malignant transformation. We focus on the principles of these techniques, review their clinical application, and compare and summarize their advantages and disadvantages. Finally, we conclude with a discussion of current challenges and future directions of this field.
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Affiliation(s)
- Y Xu
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - X Deng
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Y Sun
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - X Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Y Xiao
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Y Li
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Q Chen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - L Jiang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, Department of Oral Medicine, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Epithelial-mesenchymal transition related to bone invasion in oral squamous cell carcinoma. J Bone Oncol 2022; 33:100418. [PMID: 35242512 PMCID: PMC8881471 DOI: 10.1016/j.jbo.2022.100418] [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: 12/07/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 12/18/2022] Open
Abstract
Presence of epithelial-mesenchymal transition markers in tumor-bone interface. Dominant infiltrative pattern in bone tissue is associated with lower survival. E-cadherin-positive cases were associated with tobacco smoking. Vimentin-positive cases were associated with tumors under 4 cm. Twist could be strongly involved in bone invasion and in disease progression.
Introduction Bone invasion is an important prognostic factor in oral squamous cell carcinoma, leading to a lower survival rate and the use of aggressive treatment approaches. Epithelial-mesenchymal transition (EMT) is possibly involved in this process, because it is often related to mechanisms of cell motility and invasiveness. This study examined whether a panel of epithelial-mesenchymal markers are present in cases of oral squamous cell carcinoma with bone invasion and whether these proteins have any relationship with patients’ clinical-pathological parameters and prognostic factors. Methods Immunohistochemical analysis of E-cadherin, twist, vimentin, TGFβ1, and periostin was performed in paraffin-embedded samples of 62 oral squamous cell carcinoma cases. Results The analysis revealed that most cases (66%) presented with a dominant tumor infiltrative pattern in bone tissue, associated with lower survival rates, when compared with cases with a dominant erosive invasion pattern (P = 0.048). Twenty-seven cases (43%) expressed markers that were compatible with total or partial EMT at the tumor-bone interface. There was no association between evidence of total or partial EMT and other demographic or prognostic features. E-cadherin-positive cases were associated with tobacco smoking (P = 0.022); vimentin-positive cases correlated with tumors under 4 cm (P = 0.043). Twistexpression was observed in tumors with a dominant infiltrative pattern (P = 0.041) and was associated with the absence of periostin (P = 0.031). Conclusion We observed evidence of total or partial EMT in oral squamous cell carcinoma bone invasion. The transcription factor twist appears to be involved in bone invasion and disease progression.
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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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Goel D, Shah S, Mair M. Diagnostic Adjuncts in Oral Cancer Evaluation. Crit Rev Oncog 2022; 27:39-45. [PMID: 37199301 DOI: 10.1615/critrevoncog.2022047079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Oral cancer is a major health concern in developing countries like India which contributes one-third of the global oral cancer burden. Unlike other non-head and neck malignancies, oral cancer has a more curative treatment course. If detected early, oral cancer has the best treatment outcomes. However, most oral cancer has a dismal five-year survival rate as the majority are diagnosed in late/advanced loco-regional stages. Current methods of assessment for oral cancer include, thorough clinical examination under white light and biopsy. Over the years, a number of diagnostic tools have been created as adjuncts to white light evaluation to help with the early diagnosis of oral cancer. This article's goal is to discuss the present diagnostic techniques for oral cancer as well as potential future uses of cutting-edge, innovative technology for the detection of the disease. This may expand our diagnostic choices and enhance our capacity to accurately identify and manage lesions associated with oral cancer.
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Affiliation(s)
- Daksh Goel
- Head and Neck Department, Zydus Cancer Centre, Ahmedabad, Gujarat, India
| | - Siddharth Shah
- Head and Neck Department, Zydus Cancer Centre, Ahmedabad, Gujarat, India
| | - Manish Mair
- Head and Neck Department, University Hospital of Leicester, NHS Trust, Leicester, United Kingdom
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Jayavel K, Sivagnanam S. The Current Scenario Regarding the Narrative Advancement of Oral Cancer. CLINICAL CANCER INVESTIGATION JOURNAL 2022. [DOI: 10.51847/fehfvfwasl] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Verma G, Aggarwal N, Chhakara S, Tyagi A, Vishnoi K, Jadli M, Singh T, Goel A, Pandey D, Sharma A, Agarwal K, Sarkar U, Doval DC, Sharma S, Mehrotra R, Singh SM, Bharti AC. Detection of human papillomavirus infection in oral cancers reported at dental facility: assessing the utility of FFPE tissues. Med Oncol 2021; 39:13. [PMID: 34792663 DOI: 10.1007/s12032-021-01608-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022]
Abstract
Incidence of human papillomavirus (HPV)-associated oral cancers is on the rise. However, epidemiological data of this subset of cancers are limited. Dental hospital poses a unique advantage in detection of HPV-positive oral malignancies. We assessed the utility of formalin-fixed paraffin-embedded (FFPE) tissues, which are readily available, for evaluation of high-risk HPV infection in oral cancer. For protocol standardization, we used 20 prospectively collected paired FFPE and fresh tissues of histopathologically confirmed oral cancer cases reported in Oral Medicine department of a dental hospital for comparative study. Only short PCRs (~ 200 bp) of DNA isolated using a modified xylene-free method displayed a concordant HPV result. For HPV analysis, we used additional 30 retrospectively collected FFPE tissues. DNA isolated from these specimens showed an overall 23.4% (11/47) HPV positivity with detection of HPV18. Comparison of HPV positivity from dental hospital FFPE specimens with overall HPV positivity of freshly collected oral cancer specimens (n = 55) from three cancer care hospitals of the same region showed notable difference (12.7%; 7/55). Further, cancer hospital specimens showed HPV16 positivity and displayed a characteristic difference in reported sub-sites and patient spectrum. Overall, using a xylene-free FFPE DNA isolation method clubbed with short amplicon PCR, we showed detection of HPV-positive oral cancer in dental hospitals.
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Affiliation(s)
- Gaurav Verma
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India
- School of Biotechnology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Nikita Aggarwal
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi, New Delhi, 110007, India
| | - Suhail Chhakara
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi, New Delhi, 110007, India
| | - Abhishek Tyagi
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India
- Department of Cancer Biology, Wake Forest University of Medicine, Winston-Salem, NC, USA
| | - Kanchan Vishnoi
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India
- School of Biotechnology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Mohit Jadli
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi, New Delhi, 110007, India
| | - Tejveer Singh
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi, New Delhi, 110007, India
| | - Ankit Goel
- Subharti Dental College, Meerut, Uttar Pradesh, India
| | - Durgatosh Pandey
- Department of Oncosurgery, Dr. Bheem Rao Ambedkar Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | | | | | - Urmi Sarkar
- Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
| | | | - Shashi Sharma
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India
| | - Ravi Mehrotra
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India
| | - Sukh Mahendra Singh
- School of Biotechnology, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Alok Chandra Bharti
- Division of Molecular Oncology, ICMR- National Institute of Cancer Prevention and Research, Noida, Uttar Pradesh, India.
- Molecular Oncology Laboratory, Department of Zoology, University of Delhi, New Delhi, 110007, India.
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Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review. Cancers (Basel) 2021; 13:cancers13184600. [PMID: 34572831 PMCID: PMC8467703 DOI: 10.3390/cancers13184600] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/29/2021] [Accepted: 09/09/2021] [Indexed: 01/06/2023] Open
Abstract
The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.
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Cagna DR, Donovan TE, McKee JR, Eichmiller F, Metz JE, Albouy JP, Marzola R, Murphy KG, Troeltzsch M. Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry. J Prosthet Dent 2021; 126:276-359. [PMID: 34489050 DOI: 10.1016/j.prosdent.2021.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 11/26/2022]
Abstract
The Scientific Investigation Committee of the American Academy of Restorative Dentistry offers this review of the 2020 professional literature in restorative dentistry to inform busy dentists regarding noteworthy scientific and clinical progress over the past year. Each member of the committee brings discipline-specific expertise to this work to cover this broad topic. Specific subject areas addressed include prosthodontics; periodontics, alveolar bone, and peri-implant tissues; implant dentistry; dental materials and therapeutics; occlusion and temporomandibular disorders (TMDs); sleep-related breathing disorders; oral medicine and oral and maxillofacial surgery; and dental caries and cariology. The authors focused their efforts on reporting information likely to influence day-to-day dental treatment decisions with a keen eye on future trends in the profession. With the tremendous volume of dentistry and related literature being published today, this review cannot possibly be comprehensive. The purpose is to update interested readers and provide important resource material for those interested in pursuing greater detail. It remains our intent to assist colleagues in navigating the extensive volume of important information being published annually. It is our hope that readers find this work useful in successfully managing the dental patients they encounter.
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Affiliation(s)
- David R Cagna
- Professor, Associate Dean, Chair and Residency Director, Department of Prosthodontics, University of Tennessee Health Sciences Center College of Dentistry, Memphis, Tenn.
| | - Terence E Donovan
- Professor, Department of Comprehensive Oral Health, University of North Carolina School of Dentistry, Chapel Hill, NC
| | | | - Frederick Eichmiller
- Vice President and Science Officer, Delta Dental of Wisconsin, Stevens Point, Wis
| | | | - Jean-Pierre Albouy
- Assistant Professor of Prosthodontics, Department of Restorative Sciences, University of North Carolina School of Dentistry, Chapel Hill, NC
| | | | - Kevin G Murphy
- Associate Clinical Professor, Department of Periodontics, University of Maryland College of Dentistry, Baltimore, Md; Private practice, Baltimore, Md
| | - Matthias Troeltzsch
- Associate Professor, Department of Oral and Maxillofacial Surgery, Ludwig-Maximilians University of Munich, Munich, Germany; Private practice, Ansbach, Germany
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Chaurasia A, Alam SI, Singh N. Oral cancer diagnostics: An overview. Natl J Maxillofac Surg 2021; 12:324-332. [PMID: 35153426 PMCID: PMC8820315 DOI: 10.4103/njms.njms_130_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/02/2020] [Accepted: 12/10/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer was first mentioned in medicine texts by Egyptians. Ancient Indians studied oral cancer in great detail under Susruta. Cancer has continued to be a challenge to physicians from ancient times to the present. Over the years, cancer underwent a shift in management from radical surgeries toward a more preventive approach. Early diagnosis is vital in reducing cancer-associated mortality especially with oral cancer. Even though the mainstay of oral cancer diagnosis still continues to be a trained clinician and histopathologic examination of malignant tissues. Translating innovation in technological advancements in diagnostic aids for oral cancer will require both improved decision-making and a commitment toward optimizing cost, skills, turnover time between capturing data and obtaining a useful result. The present review describes the conventional to most advanced diagnostic modalities used as oral cancer diagnostics. It also includes the new technologies available and the future trends in oral cancer diagnostics.
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Affiliation(s)
- Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George Medical University, Lucknow, Uttar Pradesh, India
| | - Saman Ishrat Alam
- Department of Oral Medicine and Radiology, Rama Dental College, Rama University, Kanpur, Uttar Pradesh, India
| | - Navin Singh
- Department of Radiotherapy, King George Medical University, Lucknow, Uttar Pradesh, India
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Xiao J, Fiscella KA, Meyerowitz C. mDentistry: A powerful tool to improve oral health of a broad population in the digital era. J Am Dent Assoc 2021; 152:713-716. [PMID: 34454644 DOI: 10.1016/j.adaj.2021.06.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/09/2021] [Accepted: 06/01/2021] [Indexed: 12/19/2022]
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Overview of oral cavity squamous cell carcinoma: Risk factors, mechanisms, and diagnostics. Oral Oncol 2021; 121:105451. [PMID: 34329869 DOI: 10.1016/j.oraloncology.2021.105451] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/01/2021] [Accepted: 07/04/2021] [Indexed: 02/07/2023]
Abstract
Oral cavity squamous cell carcinoma (OCSCC) is the most common malignancy of the oral cavity. The substantial risk factors for OCSCC are the consumption of tobacco products, alcohol, betel quid, areca nut, and genetic alteration. However, technological advancements have occurred in treatment, but the survival decreases with late diagnosis; therefore, new methods are continuously being investigated for treatment. In addition, the rate of secondary tumor formation is 3-7% yearly, which is incomparable to other malignancies and can lead to the disease reoccurrence. Oral cavity cancer (OCC) arises from genetic alterations, and a complete understanding of the molecular mechanism involved in OCC is essential to develop targeted treatments. This review aims to update the researcher on oral cavity cancer, risk factors, genetic alterations, molecular mechanism, classification, diagnostic approaches, and treatment.
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Current Insights into Oral Cancer Diagnostics. Diagnostics (Basel) 2021; 11:diagnostics11071287. [PMID: 34359370 PMCID: PMC8303371 DOI: 10.3390/diagnostics11071287] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 06/30/2021] [Accepted: 07/13/2021] [Indexed: 12/16/2022] Open
Abstract
Oral cancer is one of the most common head and neck malignancies and has an overall 5-year survival rate that remains below 50%. Oral cancer is generally preceded by oral potentially malignant disorders (OPMDs) but determining the risk of OPMD progressing to cancer remains a difficult task. Several diagnostic technologies have been developed to facilitate the detection of OPMD and oral cancer, and some of these have been translated into regulatory-approved in vitro diagnostic systems or medical devices. Furthermore, the rapid development of novel biomarkers, electronic systems, and artificial intelligence may help to develop a new era where OPMD and oral cancer are detected at an early stage. To date, a visual oral examination remains the routine first-line method of identifying oral lesions; however, this method has certain limitations and as a result, patients are either diagnosed when their cancer reaches a severe stage or a high-risk patient with OPMD is misdiagnosed and left untreated. The purpose of this article is to review the currently available diagnostic methods for oral cancer as well as possible future applications of novel promising technologies to oral cancer diagnosis. This will potentially increase diagnostic options and improve our ability to effectively diagnose and treat oral cancerous-related lesions.
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Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021; 47:1352-1357. [PMID: 34119562 DOI: 10.1016/j.joen.2021.06.003] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/03/2021] [Accepted: 06/03/2021] [Indexed: 01/04/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions. METHODS Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review. RESULTS AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery. CONCLUSIONS AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice.
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Affiliation(s)
- Anita Aminoshariae
- Department of Endodontics, Case School of Dental Medicine, Cleveland, Ohio.
| | - Jim Kulild
- Department of Endodontics, University of Missouri-Kansas City School of Dentistry, Kansas City, Missouri
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
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Khanagar SB, Naik S, Al Kheraif AA, Vishwanathaiah S, Maganur PC, Alhazmi Y, Mushtaq S, Sarode SC, Sarode GS, Zanza A, Testarelli L, Patil S. Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11061004. [PMID: 34072804 PMCID: PMC8227647 DOI: 10.3390/diagnostics11061004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/25/2021] [Accepted: 05/29/2021] [Indexed: 12/20/2022] Open
Abstract
Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.
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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 11481, Saudi Arabia;
- King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Sachin Naik
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; (S.N.); (A.A.A.K.)
| | - Abdulaziz Abdullah Al Kheraif
- Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia; (S.N.); (A.A.A.K.)
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (S.V.); (P.C.M.)
| | - Prabhadevi C. Maganur
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (S.V.); (P.C.M.)
| | - Yaser Alhazmi
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shazia Mushtaq
- College of Applied Medical Sciences, Dental Health Department, King Saud University, Riyadh 12372, Saudi Arabia;
| | - Sachin C. Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, India; (S.C.S.); (G.S.S.)
| | - Gargi S. Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, India; (S.C.S.); (G.S.S.)
| | - Alessio Zanza
- Department of Maxillo and Oro-Facial Sciences, University of Rome La Sapienza, 00185 Rome, Italy; (A.Z.); (L.T.)
| | - Luca Testarelli
- Department of Maxillo and Oro-Facial Sciences, University of Rome La Sapienza, 00185 Rome, Italy; (A.Z.); (L.T.)
| | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
- Correspondence:
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Abstract
Oral cancer is a major public health problem, and there is an increasing trend
for oral cancer to affect young men and women. Public awareness is poor, and
many patients present with late-stage disease, contributing to high mortality.
Oral cancer is often preceded by a clinical premalignant phase accessible to
visual inspection, and thus there are opportunities for earlier detection and to
reduce morbidity and mortality. Screening asymptomatic individuals by systematic
visual oral examinations to detect the disease has been shown to be feasible. A
positive screen includes both oral cancer and oral potentially malignant
disorders. We review key screening studies undertaken, including 1 randomized
clinical trial. Screening of high-risk groups is cost-effective. Strengths and
weaknesses of oral cancer screening studies are presented to help guide new
research in primary care settings and invigorated by the prospect of using
emerging new technologies that may help to improve discriminatory accuracy of
case detection. Most national organizations, including the US Preventive
Services Task Force, have so far not recommended population-based screening due
a lack of sufficient evidence that screening leads to a reduction in oral cancer
mortality. Where health care resources are high, opportunistic screening in
dental practices is recommended, although the paucity of research in primary
care is alarming. The results of surveys suggest that dentists do perform oral
cancer screenings, but there is only weak evidence that screening in dental
practices leads to downstaging of disease. Where health care resources are low,
the feasibility of using primary health care workers for oral cancer screening
has been tested, and measures indicate good outcomes. Most studies reported in
the literature are based on 1 round of screening, whereas screening should be a
continuous process. This review identifies a huge potential for new research
directions on screening for oral cancer.
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Affiliation(s)
- S. Warnakulasuriya
- King’s College London and WHO
Collaborating Centre for Oral Cancer, London, UK
- S. Warnakulasuriya, Faculty of Dentistry,
Oral & Craniofacial Sciences, King’s College London and WHO Collaborating
Centre for Oral Cancer, London, UK.
| | - A.R. Kerr
- Department of Oral and Maxillofacial
Pathology, Radiology & Medicine, New York University College of Dentistry, New
York, NY, USA
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Kaur J, Srivastava R, Borse V. Recent advances in point-of-care diagnostics for oral cancer. Biosens Bioelectron 2021; 178:112995. [PMID: 33515983 DOI: 10.1016/j.bios.2021.112995] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 12/24/2022]
Abstract
Early-stage diagnosis is a crucial step in reducing the mortality rate in oral cancer cases. Point-of-care (POC) devices for oral cancer diagnosis hold great future potential in improving the survival rates as well as the quality of life of oral cancer patients. The conventional oral examination followed by needle biopsy and histopathological analysis have limited diagnostic accuracy. Besides, it involves patient discomfort and is not feasible in resource-limited settings. POC detection of biomarkers and diagnostic adjuncts has emerged as non- or minimally invasive tools for the diagnosis of oral cancer at an early stage. Various biosensors have been developed for the rapid detection of oral cancer biomarkers at the point-of-care. Several optical imaging methods have also been employed as adjuncts to detect alterations in oral tissue indicative of malignancy. This review summarizes the different POC platforms developed for the detection of oral cancer biomarkers, along with various POC imaging and cytological adjuncts that aid in oral cancer diagnosis, especially in low resource settings. Various immunosensors and nucleic acid biosensors developed to detect oral cancer biomarkers are summarized with examples. The different imaging methods used to detect oral tissue malignancy are also discussed herein. Additionally, the currently available commercial devices used as adjuncts in the POC detection of oral cancer are emphasized along with their characteristics. Finally, we discuss the limitations and challenges that persist in translating the developed POC techniques in the clinical settings for oral cancer diagnosis, along with future perspectives.
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Affiliation(s)
- Jasmeen Kaur
- NanoBios Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Rohit Srivastava
- NanoBios Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Vivek Borse
- NanoBioSens Laboratory, Centre for Nanotechnology, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India.
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Trebing CT, Sen S, Rues S, Herpel C, Schöllhorn M, Lux CJ, Rammelsberg P, Schwindling FS. Non-invasive three-dimensional thickness analysis of oral epithelium based on optical coherence tomography-development and diagnostic performance. Heliyon 2021; 7:e06645. [PMID: 33898808 PMCID: PMC8055558 DOI: 10.1016/j.heliyon.2021.e06645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/12/2021] [Accepted: 03/26/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives Evaluating structural changes in oral epithelium can assist with the diagnosis of cancerous lesions. Two-dimensional (2D) non-invasive optical coherence tomography (OCT) is an established technique for this purpose. The objective of this study was to develop and test the diagnostic accuracy of a three-dimensional (3D) evaluation method. Methods The oral lip mucosa of 10 healthy volunteers was scanned using an 870-nm spectral-domain OCT device (SD-OCT) with enhanced depth imaging (EDI). Four raters semi-automatically segmented the epithelial layer twice. Thus, eighty 3D datasets were created and analyzed for epithelial thickness. To provide a reference standard for comparison, the raters took cross-sectional 2D measurements at representative sites. The correlation between the 2D and 3D measurements, as well as intra- and inter-rater reliability, were analyzed using intraclass correlation coefficients (ICC). Results Mean epithelial thickness was 280 ± 64μm (range 178–500 μm) and 268 ± 49μm (range 163–425 μm) for the 2D and 3D analysis, respectively. The inter-modality correlation of the thickness values was good (ICC: 0.76 [0.626–0.846]), indicating that 3D analysis of epithelial thickness provides valid results. Intra-rater and inter-rater reliability were good (3D analysis) and excellent (2D analysis), suggesting high reproducibility. Conclusions Diagnostic accuracy was high for the developed 3D analysis of oral epithelia using non-invasive, radiation-free OCT imaging. Clinical significance This new 3D technique could potentially be used to improve time-efficiency and quality in the diagnosis of epithelial lesions compared with the 2D reference standard.
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Affiliation(s)
| | - Sinan Sen
- Department of Orthodontics, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan Rues
- Department of Prosthodontics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christopher Herpel
- Department of Prosthodontics, Heidelberg University Hospital, Heidelberg, Germany
| | - Maria Schöllhorn
- Department of Prosthodontics, Heidelberg University Hospital, Heidelberg, Germany
| | - Christopher J Lux
- Department of Orthodontics, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter Rammelsberg
- Department of Prosthodontics, Heidelberg University Hospital, Heidelberg, Germany
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Ilhan B, Guneri P, Wilder-Smith P. The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer. Oral Oncol 2021; 116:105254. [PMID: 33711582 DOI: 10.1016/j.oraloncology.2021.105254] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 02/11/2021] [Accepted: 02/24/2021] [Indexed: 02/07/2023]
Abstract
Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high rates of disease-related morbidity and mortality due to advanced loco-regional stage at diagnosis. Early detection and prompt treatment offer the best outcomes to patients, yet the majority of OC lesions are detected at late stages with 45% survival rate for 2 years. The primary cause of poor OC outcomes is unavailable or ineffective screening and surveillance at the local point-of-care level, leading to delays in specialist referral and subsequent treatment. Lack of adequate awareness of OC among the public and professionals, and barriers to accessing health care services in a timely manner also contribute to delayed diagnosis. As image analysis and diagnostic technologies are evolving, various artificial intelligence (AI) approaches, specific algorithms and predictive models are beginning to have a considerable impact in improving diagnostic accuracy for OC. AI based technologies combined with intraoral photographic images or optical imaging methods are under investigation for automated detection and classification of OC. These new methods and technologies have great potential to improve outcomes, especially in low-resource settings. Such approaches can be used to predict oral cancer risk as an adjunct to population screening by providing real-time risk assessment. The objective of this study is to (1) provide an overview of components of delayed OC diagnosis and (2) evaluate novel AI based approaches with respect to their utility and implications for improving oral cancer detection.
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Affiliation(s)
- Betul Ilhan
- Ege University, Faculty of Dentistry, Department of Oral & Maxillofacial Radiology, Bornova, Izmir, Turkey.
| | - Pelin Guneri
- Ege University, Faculty of Dentistry, Department of Oral & Maxillofacial Radiology, Bornova, Izmir, Turkey
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