Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review

Mahmood, Hanya; Shaban, Muhammad; Ruiz, Blanca Iciar Indave; Santos‐Silva, Alan Roger; Rajpoot, Nasir M.; Khurram, Syed Ali

doi:10.1016/j.oraloncology.2020.104885

Cited by 91 publications

(114 citation statements)

References 48 publications

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“…AI and computerized support, although not new to healthcare, have lately received a lot of attention within the sphere of dentistry. These reviews covered their potential dental applications [ 19 ], success in detecting precancerous lesions and metastases [ 20 ], effectiveness in improving the quality of maxillofacial radiology [ 21 ], success in orthodontic treatment [ 22 ], and orthopedic rehabilitation [ 23 ], as well as concurrent application with virtual reality to decrease anxiety in young patients [ 24 ]. However, the aforementioned reviews did not systematically explore the current diagnostic capabilities of AI in identifying common orofacial diseases and disorders and/or the subsequently elicited pain [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Farook

Jamayet

Abdullah

et al. 2021

Pain Research and Management

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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.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Farook

Jamayet

Abdullah

et al. 2021

Pain Research and Management

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“…Ideally, the reference standard should include multiple annotations from different experts to reduce subjectivity and account for interobserver variability. 23 This is particularly important in the context of AI given its potential capabilities in detecting disease more accurately than human operators. 1 Furthermore, AI may also be capable of detecting subtle changes indicative of a diagnosis through recognition of patterns not detectable by human operators, for example deriving cardiovascular risk from retinal images, identification of individuals with atrial fibrillation from their ECGs taken during sinus rhythm and identifying stromal features associated with breast cancer survival.…”

Section: Discussionmentioning

confidence: 99%

“…13 studies reported an inability to provide systematic quality assessment or evaluate certain biases as a limitation in their study (Supplementary Table 2). Specifically, these included concerns around size and quality of the dataset, including its real-world clinical applicability; for example including a whole tissue section instead of a portion of interest only 23 and providing samples from multiple centres across different demographic populations to improve generalisability of the model. Appropriate separation of data set into training, validation and test sets without overlap was also highlighted as an area needing evaluation, as overlap between datasets would lead to higher accuracy rates.…”

Section: Patient Selection -Photographicmentioning

confidence: 99%

What are the Quality Assessment Standards used in Artificial Intelligence Diagnostic Accuracy Systematic Reviews?

Jayakumar¹,

Sounderajah²,

Normahani³

et al. 2021

Preprint

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Data was extracted onto a standardised proforma by two independent reviewers (VS and SJ). Study characteristics extracted were study author, year, institution, country, journal and journal impact factor. Data was collected on use of QUADAS and/or other quality assessment tools, quality assessment tool adherence, modifications to pre-existing tools, use of multiple tools to improve applicability to AI specific studies and any limitations pertaining to quality assessment expressed by study authors.Studies were classified into four clinical categories based upon the type of sample evaluated and upon the diagnostic task: (a) axial medical imaging, (b) non-axial medical imaging, (c) histopathological digital records (digital pathology) and (d) photographic images. Key AI-related extraction items were identified through examination of recently developed AI extensions to existing quality assessment tools. A consensus was reached amongst authors to ascertain vital items. Quality AssessmentThe AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) was employed to evaluate the quality of included studies (Supplementary Table 1). 9 LowHigh Unclear

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“…In addition to several functional symptoms like teeth loss, head-neck pain and potentially fatal consequences, this craniofacial disease also likely results in disfigurement of patients without early diagnosis or favorable prognosis. Classical oral cancer detection and diagnosis are based on radiological analysis, clinical monitoring indicators and histopathological assessments ( Mahmood et al, 2020 ). Prevention and early-stage diagnosis are of great significance to the survival rate and treatment management of cancerous patients.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

“…Semantic image segmentation and feature extraction are two fundamental processes of image classification through ML methods. They form the basis of oral cancer detection by this type of approach ( Haider et al, 2020 ; Mahmood et al, 2020 ). Hyperspectral Imaging (HSI) is a currently applicable technique for tumor detection.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 91 publications

References 48 publications

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

What are the Quality Assessment Standards used in Artificial Intelligence Diagnostic Accuracy Systematic Reviews?

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

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