AIMC Topic: Wounds and Injuries
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Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization.
BMC medical informatics and decision making
Aug 9, 2025
BACKGROUND: Chronic wounds (CWs) represent a significant and growing challenge in healthcare due to their prolonged healing times, complex management, and associated costs. Inadequate wound assessment by healthcare professionals (HCPs), often due to ...
An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity.
Scientific reports
Jul 9, 2025
Accurate prediction of crash injury severity and understanding the seriousness of multi-classification injuries is vital for informing authorities and the public. This Knowledge is crucial for enhancing road safety and reducing congestion, as differe...
Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy.
Scientific reports
Jul 8, 2025
Despite advancements in trauma care, uncontrolled hemorrhage and trauma-induced coagulopathy (TIC) remain the leading causes of preventable deaths after trauma. Understanding the genetic underpinnings and molecular mechanisms of TIC is crucial for de...
Eff-ReLU-Net: a deep learning framework for multiclass wound classification.
BMC medical imaging
Jul 1, 2025
Chronic wounds have emerged as a significant medical challenge due to their adverse effects, including infections leading to amputations. Over the past few years, the prevalence of chronic wounds has grown, thus posing significant health hazards. It ...
Enhancing chronic wound assessment through agreement analysis and tissue segmentation.
Scientific reports
Jul 1, 2025
Accurate monitoring of chronic wound progression is crucial for assessing healing dynamics. However, the current manual process of tissue segmentation and quantification, which is an indicator of the healing progress, is time-consuming and subject to...
Predicting opportunities for improvement in trauma care using machine learning: a retrospective registry-based study at a major trauma centre.
BMJ open
Jun 6, 2025
OBJECTIVE: To develop models to predict opportunities for improvement in trauma care and compare the performance of these models to the currently used audit filters.
A Machine Learning Trauma Triage Model for Critical Care Transport.
JAMA network open
Jun 2, 2025
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
Gasdermin D drives the systemic storm and mortality after trauma with hemorrhage to a greater degree in biological females than males.
Science translational medicine
May 28, 2025
Severe injury accompanied by hemorrhagic shock triggers an early release of cell constituents into the circulation, referred to as the systemic storm. The systemic storm drives the systemic inflammatory response and is associated with increased morta...
Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.
International journal of injury control and safety promotion
Apr 11, 2025
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...
Intelligent progress monitoring of healing wound tissues based on classification models.
Biomedical physics & engineering express
Mar 26, 2025
The evolution of wound monitoring techniques has seen a significant shift from traditional methods like ruler-based measurements to the use of AI-assisted assessment of wound tissues. This progression has been driven by the need for more accurate, ef...
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