Ayala, Iván Nicolas

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

Ataxia is a constellation of symptoms that involves a lack of coordination, imbalance, and difficulty walking. Hereditary ataxia occurs when a person is born with defective genes, and this degenerative disorder may progress for several years. There is no effective cure for ataxia, so we need to search for new treatments. Recently, interest in riluzole in the treatment of ataxia has emerged. We conducted this systematic review to analyze the safety and efficacy of riluzole for treating hereditary ataxia in recent clinical trials. We conducted a systematic review using PubMed and Google Scholar as databases in search of this relationship. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) protocols to conduct this study. For inclusion criteria, we included full-text clinical trials on humans written in English and found three clinical trials. We excluded case reports, literature reviews, systematic reviews, and meta-analyses for this analysis. We aimed to evaluate the Scale for the Assessment and Rating of Ataxia (SARA) score, the International Cooperative Ataxia Rating Scale (ICARS) score, and the safety of the medication. Two out of the three clinical trials showed statistically significant clinical improvement in the ICARS and SARA scores, while the other trial did not show improvement in the clinical or radiological outcomes. The drug was safe in all clinical trials. Overall, the results of this analysis of riluzole for the treatment of hereditary ataxia are encouraging. Further clinical trials are needed to investigate the efficacy of riluzole on hereditary ataxia.