Military Medicine

Using machine learning methods to predict the outcome of psychological therapies for post-traumatic stress disorder: A systematic review.

BACKGROUND: A number of treatments are available for post-traumatic stress disorder (PTSD), however, there is currently a lack of data-driven treatment selection and adaptation methods for this condition. Machine learning (ML) could potentially help to improve the prediction of treatment outcomes and enable precision mental healthcare in practice.

Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.

Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health problems and socioeconomic impairments. Although the complex symptomatology of PTSD makes diagnosis difficult, early identification and intervention are crucial to mitigate the long-term effects of PTSD and provide appropriate treatment. In this study, w...

Navigating prevalence shifts in image analysis algorithm deployment.

Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current r...

Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.

Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress dis...

Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.

BACKGROUND: Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning ...

Bridging the Digital Divide: A Practical Roadmap for Deploying Medical Artificial Intelligence Technologies in Low-Resource Settings.

In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and deliv...

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personal...

Self-trainable and adaptive sensor intelligence for selective data generation.

With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challeng...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

Artificial intelligence and machine learning in veterinary medicine: a regulatory perspective on current initiatives and future prospects.

The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into i...

The Role of Artificial Intelligence in Health Care.

Artificial intelligence (AI) plays a leading role in transmuting the field of healthcare. Numerous aspects of AI have been incorporated into the healt...

Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies.

The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outco...

Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females.

Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30-60% of the r...

[Telemedicine and AI-supported diagnostics in the daily routine of visceral medicine].

Advances in telemedicine, exemplified by augmented reality (AR) and virtual reality (VR), are rapidly progressing. For instance, AR available over lon...

Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.

BACKGROUND: Home hospitalization is a care modality growing in popularity worldwide. Telemedicine-driven hospital-at-home (HAH) services could replace...

Mapping two decades of research in rheumatology-specific journals: a topic modeling analysis with BERTopic.

BACKGROUND: Rheumatology has experienced notable changes in the last decades. New drugs, including biologic agents and Janus kinase (JAK) inhibitors, ...

Ethical and Bias Considerations in Artificial Intelligence/Machine Learning.

As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI mo...

Attention and dilated convolutions inclusive deep-CNN with multiplexed texture features to diagnose Pathological and High Myopia.

In today's era, precise and timely diagnosis of ocular diseases are crucial as these disorders jeopardize millions of visions. Early detection and pro...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. ...

CALLM: Enhancing Clinical Interview Analysis Through Data Augmentation With Large Language Models.

The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automat...