Background: Fatal choking is a preventable cause of mechanical asphyxia that occurs more frequently among individuals with psychiatric disorders due to behavioural abnormalities, neurological comorbidities, and medication-related swallowing impairment. This review aims to highlight the clinical and forensic factors contributing to fatal choking and the importance of a multidisciplinary approach to its investigation. Methods: A systematic review was conducted according to PRISMA guidelines by searching PubMed, Scopus, and Web of Science. In total, 25 studies describing fatal choking in adults with psychiatric disorders and including medico-legal investigations were selected and analyzed. Results: Schizophrenia was the most frequently reported psychiatric condition, followed by bipolar disorder, depression, and dementia. Antipsychotics, antidepressants, anxiolytics, and other psychotropic drugs were recurrently associated with dysphagia and reduced airway protective reflexes. Food boluses represented the predominant obstructive material, although several unusual foreign bodies were documented. Virtopsy, autopsy, histological, and toxicological investigations consistently proved essential for confirming airway obstruction, reconstructing the mechanism of death, and identifying contributing clinical and pharmacological factors. Conclusions: Fatal choking in psychiatric patients is a multifactorial event requiring early risk assessment, preventive strategies, and comprehensive forensic evaluation to improve patient safety and ensure accurate determination of the cause of death.

Background: Artificial intelligence (AI) is increasingly applied in medical image analysis, although its application in forensic pathology remains limited. The assessment of ligature marks in hanging deaths is challenging and relies on forensic expertise. This pilot study evaluated a deep learning approach for morphological classification of hanging-related ligature marks. Methods: A Convolutional Neural Network (CNN) was trained on a dataset of 404 standardized JPEG images obtained from forensic medicine atlases and classified into hanging-related ligature marks and non-hanging lesions, including strangulation and post-mortem artefacts. Following internal validation, the model was tested on an independent set of forensic case images provided by forensic pathology experts from Messina, Italy, and Vilnius, Lithuania. Images were annotated and reviewed by a team of two forensic pathologists, with final labels assigned by consensus using morphological criteria. Results: The CNN demonstrated encouraging classification performance with an F1-score of 0.81 ± 0.04, distinguishing hanging-related ligature marks from morphologically similar lesions. The methodological framework and image standardization criteria for AI-assisted forensic analysis were also established. Conclusions: AI-based image analysis may support the evaluation of ligature marks during external examinations. Nevertheless, forensic diagnosis requires the integration of autopsy findings, physical examination, and circumstantial evidence. Larger datasets and multicenter protocols are needed to further assess reliability and applicability.