Research Insights in American hhm – American Healthcare Magazine

A lightweight alignment-aware DBNet for surgical instrument code detection

Reliable detection of engraved surface codes on surgical instruments is essential for endtoend traceability yet remains challenging in practice because metallic reflection motion blur scale variation

AI-assisted forecasting in microsurgery: A dual-component framework for global publication trends

Artificial intelligence AIassisted approaches may allow surgical research trends to be analyzed at scale and projected over time However their use in forecasting the evolution of microsurgical scholarship

Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis

Primary malignant bone tumours of the skeleton have a great diversity in their biological behaviour and the most common in adolescence is osteosarcoma for which the diagnosis and therapeutic

Comparative analysis of machine learning techniques for cardiovascular disease prediction

Early and accurate prediction of cardiovascular disease CVD is fundamental for reducing morbidity and mortality Machine learning ML algorithms provide a datadriven actionable foundation

Synthetic data augmentation for CT-based emphysema subtype classification: A comparative evaluation of generative and classical approaches

Data scarcity is a persistent challenge in medical image analysis Synthetic data generation using deep generative models has been proposed as a potential approach to address this limitation

Inclusive mobile brain-body imaging achieves equivalent EEG data quality across racial groups

Electroencephalography EEG research systematically excludes participants with textured hair limiting generalizability While inclusive hardware offers a solution it remains unvalidated in dynamic settings This study bridges this ecological gap by determining if equitable data quality is achievable across racial groups during a complex Mobile

AI alignment in medical imaging: Unveiling hidden biases through counterfactual analysis

Machine learning ML systems for medical imaging have demonstrated remarkable diagnostic capabilities However their susceptibility to learning spurious correlations with sensitive attributes poses significant risks to fairness and safety In this paper we introduce a novel statistical framework to evaluate the dependency of medical imaging

Automated identification of keratinocyte cancers in pathology reports using large language models

Keratinocyte cancers KCs are the most prevalent cancers in whiteskinned individuals yet remain underrepresented in cancer registries because reporting requirements differ greatly across jurisdictions Manual extraction of KC subtypes from medical reports is laborintensive and timeconsuming particularly as reports often document multiple

Impact of the WellCheck smartphone app linked to electronic health records on clinical outcomes in patients with type 2 diabetes: Study protocol for primary care-based

Diabetes obesity and dyslipidemia are significant contributors to cardiovascular diseases necessitating effective strategies to manage blood glucose blood pressure and lipid levels through lifestyle interventions and pharmacotherapy This study aims to assess the glycemic control and metabolic health outcomes achieved

Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients

To evaluate machine learningbased models for predicting allcause mortality in pediatric ICU patients using comprehensive biochemical panels with a focus on addressing missing data and class imbalance