Military Medicine

Preoperative considerations for nonoperating room anesthesia.

PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance of patient selection, risk stratification, and comprehensive preoperative evaluation to ensure safe anesthetic care for increasingly complex patients. RECENT FINDINGS: The volume of NORA procedures has risen significantly, with patients often present...

Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.

Structure-based deep learning models for protein-ligand binding affinity prediction (PLBAP) are commonly benchmarked using experimentally resolved co-crystal structures, but real use cases often rely on computed inputs (docked or predicted complexes). To quantify this benchmark-to-deployment mismatch, we compared the CASF-2016 performance of five reproducible PLBAP pipelines across crystal structu...

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

Edge AI holds great potential for extending the use of artificial neural networks to resource-constrained edge devices, such as microcontrollers. Desp...

Hybrid-domain attention learning for lightweight underwater image enhancement and real-time vision deployment.

Underwater image enhancement is critical for marine exploration and robotic vision. However, real-world underwater images suffer from color distortion...

Automated Summarization of Military Medical Records Using AI: Evaluation Via the OPTICA Framework.

INTRODUCTION: Military medical fitness evaluations require physicians to rapidly review extensive and heterogeneous medical records to determine servi...

MindGrab: A Spectrally-Motivated Architecture for Accessible Deep Learning in Neuroimaging.

Deployment complexity and specialized hardware requirements hinder the adoption of deep learning models in neuroimaging. We present MindGrab, a lightw...

Personalized talent cultivation and academic prediction framework for higher education based on the HA-GNN-LSTM architecture.

To address the dilemma of homogeneous talent training and the efficiency bottleneck of human resource management in universities, this study proposes ...

Performance evaluation of AkidaNet converted to spiking domain for the classification of weeds in cotton fields.

The infestation of weeds in cotton fields poses a serious threat to the production of lint. Weeds are eliminated either by spraying weed kill sprays, ...

Dynamic convolutional neural networks for altitude aware UAV object detection.

Object detection in Unmanned Aerial Vehicles (UAVs) is inherently challenging due to the wide variation in altitudes and viewpoints, coupled with the ...

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.

Plant diseases cause 20-40% annual crop losses worldwide, yet conventional detection methods remain slow, subjective, and inaccessible to smallholder ...

From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.

BACKGROUND: Artificial intelligence models for acute kidney injury (AKI) prediction achieve strong discriminative accuracy, yet clinical adoption rema...

From technology adoption to noise reduction: Behavioral and psychological dimensions of clinical artificial intelligence deployment.

For more than five decades, patients with the same condition have received markedly different care depending on which clinician they happen to see. Th...

Learning-based orchestration for low-latency AI deployment in hybrid cloud-edge platforms.

The rapid growth of AI-driven applications in hybrid cloud-edge environments poses substantial challenges to ensuring low latency, high throughput, an...

Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice.

Artificial intelligence medical devices are increasingly deployed in clinical practice, yet practical approaches to post-deployment monitoring remain ...

Novel technologies and innovations in postoperative follow-up after regional anesthesia.

PURPOSE OF REVIEW: Postoperative follow-up after regional anesthesia is essential for identifying complications, distinguishing expected block effects...

Neuroimmune mechanisms in PTSD: inflammatory heterogeneity, circuit dysfunction, and biomarker-based subtypes.

Post-traumatic stress disorder (PTSD) is characterized by exaggerated fear response, anxiety, hyperarousal and sleep disturbances. One of the major pa...

A selective deep learning framework for pressure injury staging with calibrated confidence and automated clinical documentation.

OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...

Clinical Implementation of Opportunistic Screening for Osteoporosis.

Opportunistic screening leverages existing imaging examinations performed for unrelated routine clinical indications to systematically extract quantit...

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the ...

Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.

PURPOSE OF REVIEW: This review synthesizes recent progress in applications of artificial intelligence to heart failure care, including phenotyping, ri...