The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer

İlhan, Betül; Wilder‐Smith, Petra

doi:10.1016/j.oraloncology.2021.105254

Cited by 56 publications

(34 citation statements)

References 93 publications

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“…Recently, Ilhan et al highlighted the role that AI could play in reducing in oral cancer diagnosis delay, especially telemedicine in low-resource settings. [ 22 ]. To the question raised in this review about the concordance between oral disease explorers and specialists at the cancer center, to recognize OPMD lesions, the answer is that this agreement exists in 100% when the explorers are dentists but has a predictive value of 45% when they are frontline health workers [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…AI has led to significant advances and developments in oncology [ 17 , 18 , 19 ]. Different narrative reviews have been published in relation to their usefulness for facilitating the early diagnosis of OPMD and oral cancer [ 22 ], and for the support they provide for the same purpose, as well as radiological, endoscopic, spectrometric or histological images [ 23 , 24 , 25 ]. In this regard, the present study was carried out to provide a scoping review of the application of AI to the early diagnosis of oral cancer using non-invasive techniques as well as the proposal for future investigations.…”

Section: Introductionmentioning

confidence: 99%

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Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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“…Screening protocols and better continued education strategies contribute to an early OC diagnosis. 10,35,36 c Dental Specialties Centers.…”

Section: Discussionmentioning

confidence: 99%

“…7,8 This may be due to late diagnosis and delayed treatment start. 9,10 Thus, despite the universal access that is constituent to the Brazilian Unified Health System (acronym in Portuguese, SUS), some challenges still need to be overcomed in establishing an effective OC care network. 11 Prior studies carried out in Brazil indicated that a good service structure is associated with better work processes in primary health care (PHC).…”

Section: Introductionmentioning

confidence: 99%

Advances and weaknesses of the work process of the oral cancer care network in Brazil: A latent class transition analysis

Thomaz

Costa

Queiroz

et al. 2021

Comm Dent Oral Epid

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Objective To analyse the provision of oral cancer (OC) care services in the Dental Specialties Centers (Centros de Especialidades Odontológicas‐CEO) in Brazil and identify changes over two cycles of external evaluation of the Program for the Improvement of Access and Quality‐PMAQ, in 2014 and 2018. Method This is a nationwide panel ecological study, including 916 CEO. Data from interviews with managers and dentists of the CEO were used, including variables related to training on OC, clinical protocols, biopsies, referral for diagnosis and treatment, and registration of users with OC. We carried out Latent Transition Analysis (LTA) to identify patterns (latent status LS) of service adequacy and work processes’ changes between the two assessment cycles. We tested models with three, four, and five LS, selecting the one with the best conceptual interpretability and good model fit parameters. Data from the LS were plotted on choropleth and hotspots maps in Brazil allowing us to identify areas with the better or worse provision of specialized OC services. Results The model with four LS was chosen. The four LS were named: 1.’Most indicators inadequate for OC care’ (the worst); 2. ‘Most indicators suitable for OC care’ (the best); 3. ‘CEO with a poor relation with Primary Health Care (PHC) services’; and 4. ‘CEO with a poor relation with tertiary hospital services’. The comparison of the LS transition between the two cycles revealed that 419 (45.7%) CEO remained in the same LS (1→1, 3→4, 2→2); 228 (24.9%) switched to a worse status (2→1, 2→4, 3→1) and 269 (29.4%) switched to a better LS (1→2, 1→4, 3→2). While the majority of the CEO improved, we identified a decline of 17.8% in those who reported performing biopsies and 18.3% in the number of CEO that had hospitals for referring confirmed OC cases. Almost all Brazilian states had CEO that improved the work process. The Southeast and South regions had the highest percentage of CEO with the better work process in both cycles. Hotspots showed areas concentrating improvements in the work process in the Northeast region. However, some hotspots in the North revealed some CEO where the work process deteriorated or remained unsatisfactory. Conclusions There are regional inequities in the provision of OC care in CEO. Most services improved their work process or remained stable. However, the biopsies and the referral to hospital care for confirmed cases declined, indicating that CEO need to improve planning and care provision to reduce OC morbimortality.

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“…AI-based technology with clinical decision making or diagnoses systems can be useful modalities in oral cancer screening, lesion discrimination, and prediction model [ 102 ]. In the oral cancer screening application, AI has been shown to facilitate remote healthcare interactions in low- and middle-income countries.…”

Section: Developing Future Technologiesmentioning

confidence: 99%

Current Insights into Oral Cancer Diagnostics

Su¹,

Chen²,

Tsai

et al. 2021

Diagnostics

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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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The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer

Cited by 56 publications

References 93 publications

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Advances and weaknesses of the work process of the oral cancer care network in Brazil: A latent class transition analysis

Current Insights into Oral Cancer Diagnostics

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