Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease‐related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi‐omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Noma is a spreading and fulminant disease believed to be native to Sub-Saharan Africa over the last decade and associated with low socioeconomic status of citizens of the region. Within this noma belt, most epidemiological reports regarding the disease have emanated from the north western region of Nigeria. However, our indigenous surgical mission encountered a substantial number of cases of noma and post-noma defects noteworthy of epidemiological representation across north central Nigeria. All noma cases encountered within the 8-year study period were included and divided based on clinical signs into acute and sequelae groups. Incidence estimation was based on acute/recently active cases and was calculated using the statistical method proposed by the WHO Oral Health Unit (1994). Period prevalence of noma was calculated considering the population at risk in the zone. A total of 78 subjects were included in the study with age ranging from 2–75 years. Twelve subjects (15.4%) presented with acute disease while 66 (84.6%) had various forms of post-noma defects. The estimated incidence of noma in the north central zone was 8.3 per 100000 with a range of 4.1–17.9 per 100000 across various states. Period prevalence of noma which incorporated all cases seen within the study period was 1.6 per 100000 population at risk. Although noma may be more prevalent in the north western region of Nigeria, substantial number of cases occurs within the north central zone which calls for deliberate public awareness campaign on disease risk factors and prevention, and education of primary health-care providers. Noma, a devouring disease of the orofacial complex, is commonly associated with poverty and impoverished regions of the world especially Sub-Saharan Africa termed the noma belt region of the world. With more reports advocating for full inclusion of noma in the WHO Neglected Tropical Diseases (NTDs) program, the apparent neglect of the disease in north central Nigeria compared to other sub-regions is worrisome as the disease burden in the sub-region has not been reported till date. In this light, a retrospective, cross-sectional survey was conducted to provide epidemiological representation to the cases encountered within an eight-year period by the Cleft and Facial Deformity Foundation (CFDF), an indigenous surgical mission. The incidence of noma was estimated using methods recommended by WHO while the period prevalence was calculated considering the population living below poverty line in the sub-region. This study extrapolates an incidence of 8.3 cases per 100000 and a period prevalence of 1.6 per 100000 persons at risk. Notable is the finding that most individuals encountered were above thirty years of age and suffered varying degree of facial disfigurement consequent to acute noma disease experienced in their childhood/early adolescence. Therefore, we advocate public awareness on the disease risk factors and prevention within the sub-region as well as training of primary health personnel on disease identification, primary care and nearest referral centres. We also identify the need to bolster the efforts of existing health facilities and indigenous surgical missions in the management and rehabilitation of noma cases and survivors.

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing. Reads with a minimum of 10× coverage were used to identify differentially methylated CpG sites (DMCs) and 100 bp regions (DMRs). The performance of eight ML models and three feature-selection methods (ANOVA, MRMR, and LASSO) were then compared to determine the optimal biomarker models based on DMCs and DMRs. A total of 1745 DMCs and 105 DMRs were identified for detecting OSCC. The proportion of hypomethylated and hypermethylated DMCs was similar (51% vs. 49%), while most DMRs were hypermethylated (62.9%). Furthermore, more DMRs than DMCs were annotated to promoter regions (36% vs. 16%) and more DMCs than DMRs were annotated to intergenic regions (50% vs. 36%). Of all the ML models compared, the linear SVM model based on 11 optimal DMRs selected by LASSO had a perfect AUC, recall, specificity, and calibration (1.00) for OSCC detection. Overall, genome-wide DNA methylation techniques can be applied directly to saliva samples for biomarker discovery and ML-based platforms may be useful in stratifying OSCC during disease screening and monitoring.