Faculty Bibliography

Oral medicine is the dental specialty dedicated to the oral health care of medically complex patients and the diagnosis and management of medically related diseases, disorders, and conditions affecting the oral and maxillofacial region. Like other dental and medical specialties, oral medicine patient care is often impacted by challenges such as limited manpower, time, or resources. Artificial intelligence (AI) tools seek to supersede these challenges by automating human tasks and ushering greater efficiency and productivity. For direct patient care in oral medicine, AI has applications in risk prediction modeling, diagnosis establishment, treatment decision-making, and prognosis and outcomes prediction modeling.

Oral medicine is the dental specialty dedicated to the oral health of medically complex patients and the diagnosis and management of medically-related diseases, disorders, and conditions affecting the oral and maxillofacial region. In addition to direct patient care, Oral Medicine specialists often engage in indirect patient care activities such as patient education and practice administration and/or academic activities such as student education and research. Artificial intelligence (AI) tools have been increasingly studied to facilitate these domains and contribute to more positive outcomes for practitioners, patients, and students alike. A review of the literature on these AI applications in Oral Medicine and related medical and dental fields provides an understanding of their current advantages and limitations.

BACKGROUND:Inferring risk for malignant transformation (MT) in patients with lesions diagnosed as mild or moderate oral epithelial dysplasia (low-grade OED) remains challenging. We developed two models assessing the risk of progression to high-grade OED (severe dysplasia or carcinoma in situ) or OSCC in patients with low-grade OED lesions. METHODS:We included demographic, risk habit and clinical data from participants with low-grade OED lesions enrolled in the BC Oral Cancer Prevention Program's Oral Cancer Prediction Longitudinal study. Cox proportional hazard models were fit to estimate the effects of anatomic site and toluidine blue findings and adjusted for confounders, as both are associated with MT in the literature but without a North American-specific cohort analysis. Our primary model included both variables of interest. A secondary model included only anatomic site since toluidine blue is not in widespread use. RESULTS:Five hundred and thirty-four participants with 605 lesions met final inclusion criteria, with 339 mild and 266 moderate OED at baseline. In the primary model, lesions at a high-risk anatomic site or with positive toluidine blue staining were associated with a 2.6 and 2.4-fold increased risk of progression, respectively. In the second model that did not incorporate toluidine blue, high-risk anatomic site remained a highly associated risk factor (2.7-fold increased risk of progression). CONCLUSION/CONCLUSIONS:Lesion anatomic site is associated with higher risk of MT for the general practitioner, while a specialist with access to toluidine blue results can assume additional risk associated with positive staining. These models may inform decisions for surveillance and intervention for OED.

We conducted an umbrella review to synthesize the evidence on the diagnostic performance of liquid biopsy tests for detection of head and neck squamous cell carcinoma (HNSCC). Systematic reviews (SRs) were searched in Medline, Embase, and Google Scholar through December 6, 2023. The Joanna Briggs Institute Critical Appraisal Tool for Systematic Reviews was used to assess methodological quality. Two independent reviewers extracted data. We examined the pooled sensitivity and specificity of biomarker classes. We also statistically pooled sensitivity and specificity of individual biomarkers for oral SCC in cases where meta-analysis was not yet published, since most HNSCC occurs in the oral cavity. Performance was also assessed by specimen type (saliva, serum, plasma, and whole blood). Thirty-one SRs met inclusion criteria and 21 included meta-analyses on transcriptomic, proteomic, genomic, or metabolomic biomarkers. Overall methodologic quality was moderate to high. Primary study overlap was ≥ 15 % in 9.3 % of SR pairwise comparisons. MicroRNA (miRNA) was the biomarker class represented in the most SRs (n = 19) and individual studies (n = 106). Among these, the highest sensitivity was 77 % (95 % CI, 68-84 %), observed in miRNA-21. Hypermethylated DNA was the biomarker class with the highest pooled sensitivity (86 %; 95 % CI, 60-96 %) and specificity (92 %; 95 % CI, 80-97 %) overall, and with superior performance when used in panels compared to individual markers. In studies focused on OSCC detection, no other biomarker class or fluid type demonstrated superior performance over others. In future clinical studies, panels including hypermethylated DNA merit more rigorous evaluation to establish high specificity in addition to sufficient sensitivity.

Diagnostic adjuncts for oral potentially malignant disorders such as leukoplakia or erythroplakia can aid the clinician in triaging abnormal lesions and facilitate both biopsy site selection and surgical management. No adjuncts replace gold standard biopsy and histopathological examination, and their optimal use requires training and experience. This article covers the potential applications, both in primary and expert settings, of adjuncts, such as tissue autofluorescence, toluidine blues staining, and cytopathology. It covers new and emerging adjuncts such as confocal microscopy, liquid biopsy, oral microbiome testing, and the role of artificial intelligence. Incisional biopsy site selection and techniques will also be discussed.

UNLABELLED: HIGHLIGHTS/UNASSIGNED:A new oral cancer simulation model with risk factors including degrees of smoking and alcohol exposure, oral lesion features, and sex incorporates more accurate and precise representation of patient risk categories.We evaluated screening strategies for oral potentially malignant disorders with or without risk-stratified biopsy referral in both the general population and subpopulations defined by degrees of smoking and alcohol exposure.Men with a high degree of both smoking and alcohol exposure exhibited a significant reduction in cancer-specific deaths and cancer incidence from screening programs for oral potentially malignant disorders.Screening with risk-stratified biopsy, using a surgical treatment threshold of moderate dysplasia or worse, yielded the greatest efficiency in term of biopsies needed to detect 1 treatable case.

Oral cavity cancer is associated with high morbidity and mortality, particularly with advanced stage diagnosis. Oral cavity cancer, typically squamous cell carcinoma (OSCC), is often preceded by oral potentially malignant disorders (OPMDs), which comprise eleven disorders with variable risks for malignant transformation. While OPMDs are clinical diagnoses, conventional oral exam followed by biopsy and histopathological analysis is the gold standard for diagnosis of OSCC. There is vast heterogeneity in the clinical presentation of OPMDs, with possible visual similarities to early-stage OSCC or even to various benign oral mucosal abnormalities. The diagnostic challenge of OSCC/OPMDs is compounded in the non-specialist or primary care setting. There has been significant research interest in technology to assist in the diagnosis of OSCC/OPMDs. Artificial intelligence (AI), which enables machine performance of human tasks, has already shown promise in several domains of medical diagnostics. Computer vision, the field of AI dedicated to the analysis of visual data, has over the past decade been applied to clinical photographs for the diagnosis of OSCC/OPMDs. Various methodological concerns and limitations may be encountered in the literature on OSCC/OPMD image analysis. This narrative review delineates the current landscape of AI clinical photograph analysis in the diagnosis of OSCC/OPMDs and navigates the limitations, methodological issues, and clinical workflow implications of this field, providing context for future research considerations.

OBJECTIVE:Oral cancer examinations seek to clinically identify early oral cancers and discover premalignancies prior to their progression to invasive cancer. METHOD AND MATERIALS/METHODS:A cross-sectional study was conducted between April and August 2017 to explore and quantify the diagnostic approach used by United States (US) general dental practitioners following discovery of an oral lesion suspicious for malignancy/premalignancy. US licensed general dental practitioners who were clinically active members of the US National Dental Practice-Based Research Network were eligible to participate. Data for analysis were obtained via two questionnaires. RESULTS:The majority of participants were males, practiced full-time, had a suburban primary practice, were born during the 1950s, and graduated dental school in the 1980s or 2000s. After identifying an oral lesion suspicious for premalignancy/malignancy, the next action of most general dental practitioner respondents (65.4%) was to refer the patient for consultation/biopsy. Most general dental practitioners (87.2%) referred to an oral and maxillofacial surgeon; 22% of general dental practitioners reported personally biopsying suspicious lesions. There was a relatively weak association between completing an Advanced Education in General Dentistry or General Practice Residency and subsequently personally performing biopsies on patients with oral lesions suspicious for malignancy/premalignancy (OR 1.33, P = .06). Most written referrals took place electronically and often included information, including lesion location (87.0%), lesion sign/symptoms (85.3%), lesion history (83.9%), radiographs (59.3%), medical history (50.6%), dental history (48.8%), and photographs (42.4%). When a referral biopsy was performed, referring clinicians were most frequently informed of the findings via a written report (96.7%, when positive for malignancy/premalignancy, and 95.4% when negative). CONCLUSION/CONCLUSIONS:A snapshot is presented of current actions taken by US general dental practitioners following the discovery of oral abnormalities suspicious for premalignancy/malignancy.

OBJECTIVE:This study utilized bibliometric methodologies to explore the body of research presented at the World Workshop on Oral Medicine (WWOM) over the past 35 years. METHODS:A systematic tracking of articles across 8 editions of WWOM was conducted using databases such as Web of Science, Scopus, and PubMed. Comprehensive bibliographic analysis and data visualization were executed through Bibliometrix, Rstudio, and VOSviewer, facilitating an in-depth examination of citation metrics and collaborative networks. RESULTS:The investigation revealed that WWOM, held in 6 cities spanning four countries and addressing 33 distinct topics, demonstrated an annual citation growth rate of 8.29%, with an average of 49.31 citations per article. Notably, WWOM IV garnered the highest number of citations (1,342), reaching its peak in 2021 with 365 citations. Key contributors to the most-cited articles include Al-Hashimi, Dawes, and Syrjänen et al. Since its inception, the workshops have engaged 505 faculty members from 37 nations, with significant participation from the Americas and Europe, notably the United States and the United Kingdom, which also led in terms of publications. Network analysis delineated 6 country clusters, underscoring robust connections between the US and the UK. Co-authorship analysis revealed 18 clusters involving 267 authors. CONCLUSION/CONCLUSIONS:The influence of the WWOM on the field of oral medicine is clearly demonstrated by the outcomes of its publications, reflecting a collaborative effort to enhance comprehension and treatment modalities for patients with oral diseases. The study's findings provide insights into future research directions within the WWOM legacy.