A predictive model for swallowing dysfunction after oral cancer resection

Effect of a Personalized Enteral Nutrition Protocol on the Postoperative Nutritional Status in Patients Who Underwent Oral Cancer Surgery

Differences in the implementation of the perioperative nutrition guidelines and pathways by caregivers lead to differences in enteral nutrition provided to the patients. This study investigated the effect of a personalized enteral nutrition protocol on the postoperative nutritional status of patients who underwent oral cancer surgery at Zhejiang Ningbo NO. 2 Hospital. Those who underwent surgery between July 2017 and October 2018 received routine enteral nutrition based on the Nutritional Risk Screening 2002 (routine group), while those between November 2018 and August 2021 received personalized enteral nutrition based on the Patient-Generated Subjective Global Assessment tool (personalized group). Seventy patients completed the study (routine group, n = 34; personalized group, n = 36). After surgery, the personalized group exhibited significantly greater improvements in serum albumin levels (P < 0.01) (on the 7th and 10th day) and hand grip strength (P < 0.01), higher self-care ability (P < 0.01), lower incidence of gastrointestinal reactions during enteral feeding (P < 0.05), and shorter hospital stay (P < 0.05) than the routine group. Therefore, a personalized enteral nutrition program might improve the postoperative nutritional status, shorten the hospital stay, and accelerate postoperative recovery in patients who underwent oral cancer surgery.

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis

Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this systematic review and meta-analysis, we provide an update on current progress of ML algorithms in point-of-care (POC) automated diagnostic classification systems for lesions of the oral cavity. Studies reporting performance metrics on ML algorithms used in automatic classification of oral regions of interest were identified and screened by 2 independent reviewers from 4 databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 35 studies were suitable for qualitative synthesis, and 31 for quantitative analysis. Outcomes were assessed using a bivariate random-effects model following an assessment of bias and heterogeneity. 4 distinct methodologies were identified for POC diagnosis: (1) clinical photography; (2) optical imaging; (3) thermal imaging; (4) analysis of volatile organic compounds. Estimated AUROC across all studies was 0.935, and no difference in performance was identified between methodologies. We discuss the various classical and modern approaches to ML employed within identified studies, and highlight issues that will need to be addressed for implementation of automated classification systems in screening and early detection.