Head and Neck Cancer in Pan-American Notable People: An International Survey.

The study of notable people as advocates for raising cancer awareness began in the latter decades of the 20th century. This research aimed to identify Pan-American notable people with head and neck cancer (HNC) and to explore senior health professionals' perspectives on communicating stories of notable patients with HNC to promote prevention. A cross-sectional survey was conducted using an online questionnaire designed in REDCap and administered to 32 senior health professionals with long-standing academic and clinical backgrounds in HNC.

Mapping oral medicine (stomatology) and oral and maxillofacial pathology international organizations: a scoping review of global data and historical analysis.

Objectives: To describe the historical evolution and dissemination of the Oral Medicine and Oral and Maxillofacial Pathology international societies and associations across the globe, and to provide insights into their significant contributions toward oral health promotion.

Study Design: This review was conducted in accordance with the JBI Scoping Review Methodology Group guidance. The reporting followed the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR).

Conspiracy of Silence in Head and Neck Cancer Diagnosis: A Scoping Review.

Cancer disclosure represents a complex healthcare dynamic. Physicians or caregivers may be prompted to withhold diagnosis information from patients. This study aims to comprehensively map and synthesize available evidence about diagnosis nondisclosure regarding head and neck cancer (HNC) patients.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

Background: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298).

Methods: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest).

Barriers to early diagnosis and management of oral cancer in Latin America and the Caribbean.

Objective: This study aimed to explore perceived barriers to early diagnosis and management of oral cancer, as well as potential pathways for improvement in Latin America and the Caribbean (LAC).

Methods: This cross-sectional study used a self-administered online questionnaire created via the Research Electronic Data Capture platform. The survey was distributed to health professionals trained in Oral Medicine, Oral Pathology, Oral and Maxillofacial Surgery, and Dentists with clinical and academic expertise in oral potentially malignant disorder (OPMD) and oral cancer.

The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal.

Background: Dysplasia grading systems for oral epithelial dysplasia are a source of disagreement among pathologists. Therefore, machine learning approaches are being developed to mitigate this issue.

Methods: This cross-sectional study included a cohort of 82 patients with oral potentially malignant disorders and correspondent 98 hematoxylin and eosin-stained whole slide images with biopsied-proven dysplasia.

Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning.

Objective: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variability when assessing these lesions. The hypothesis was that different interpretations could affect the quality of the annotations used to train a Supervised Learning model.

Study Design: Forty-six clinical images from 37 patients were reviewed, classified, and manually annotated at the pixel level by 3 labelers.

Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis.

Introduction: The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304).

Methods: The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria.