Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review
Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Pain Research and Management |
| Online Access: | http://dx.doi.org/10.1155/2021/6659133 |
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| _version_ | 1832550468801265664 |
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| author | Taseef Hasan Farook Nafij Bin Jamayet Johari Yap Abdullah Mohammad Khursheed Alam |
| author_facet | Taseef Hasan Farook Nafij Bin Jamayet Johari Yap Abdullah Mohammad Khursheed Alam |
| author_sort | Taseef Hasan Farook |
| collection | DOAJ |
| description | Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain. |
| format | Article |
| id | doaj-art-1662be3d20894d9e9fa06fdf5b8d80bc |
| institution | Kabale University |
| issn | 1203-6765 1918-1523 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Pain Research and Management |
| spelling | doaj-art-1662be3d20894d9e9fa06fdf5b8d80bc2025-02-03T06:06:39ZengWileyPain Research and Management1203-67651918-15232021-01-01202110.1155/2021/66591336659133Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic ReviewTaseef Hasan Farook0Nafij Bin Jamayet1Johari Yap Abdullah2Mohammad Khursheed Alam3Maxillofacial Prosthetic Service, Prosthodontic Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kelantan 16150, MalaysiaDivision of Clinical Dentistry (Prosthodontics), School of Dentistry, International Medical University, Kuala Lumpur 57000, MalaysiaCraniofacial Imaging and Additive Manufacturing Laboratory, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kelantan 16150, MalaysiaCollege of Dentistry, Jouf University, Sakaka, Saudi ArabiaPurpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.http://dx.doi.org/10.1155/2021/6659133 |
| spellingShingle | Taseef Hasan Farook Nafij Bin Jamayet Johari Yap Abdullah Mohammad Khursheed Alam Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review Pain Research and Management |
| title | Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review |
| title_full | Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review |
| title_fullStr | Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review |
| title_full_unstemmed | Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review |
| title_short | Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review |
| title_sort | machine learning and intelligent diagnostics in dental and orofacial pain management a systematic review |
| url | http://dx.doi.org/10.1155/2021/6659133 |
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