Journal of Engineering and Science in Medical Diagnostics and Therapy

Experimental Characterization of Transient G-Forces in Spinal Fixation During Set Screw Failure

2026-08-23T08:03:20Z

Experimental Characterization of Transient G-Forces in Spinal Fixation During Set Screw Failure Hassan, Motaz; Wasir, Amanpreet Singh; Mahajan, Ajay; Chu, Tsuchin Abstract. Pedicle screw fixation systems are essential for spinal stabilization, yet the transient g-forces generated during manual set screw torquing, a critical phase with implications for implant stability, remain poorly understood. This study employs a multimodal experimental approach to quantify these dynamic forces and validate theoretical torque failure models. A sawbone spinal construct was instrumented with accelerometers at biomechanically strategic locations (screw head, spinal center, contralateral pedicle, and surrounding media) to capture transient accelerations during screw fracture. High-speed imaging (40,000 fps) and motion tracking complemented accelerometer data, while distortion energy theory (DET) and fully plastic torque (FPT) models predicted break-off torque. Results revealed extreme g-forces (up to 832 g) localized at the screw head, attenuating rapidly (20-fold reduction at the spinal center). Theoretical predictions (DET: 11.08 N·m; FPT: 11.1 N·m) aligned closely with experimental torque wrench measurements (11.3 N·m, <1.3% error), validating analytical models. Digital image analysis confirmed screw geometry precision (<1.3% error). While the rigid sawbone model limited physiological fidelity, findings emphasize the localized stress propagation and energy dissipation during screw failure, critical for optimizing implant designs, particularly in osteoporotic bone. This integrated methodology bridges biomechanical theory and experimental validation, offering actionable insights to mitigate screw loosening risks and enhance spinal construct durability. Future work will focus on advanced synthetic bone analogs and clinical correlation to refine translational relevance.

Influence of Impact Location and Incident Angle on Thoracic Injury Risk From Kinetic Impact Projectiles

2026-08-23T08:03:19Z

Influence of Impact Location and Incident Angle on Thoracic Injury Risk From Kinetic Impact Projectiles Anzir, Arafat; Muci-Küchler, Karim H. Abstract. Kinetic impact projectiles (KIPs) are widely utilized in law enforcement as a nonlethal means for crowd control, yet they remain capable of causing severe or fatal injuries. This study utilizes the total human model for safety (THUMS) finite element model developed by Toyota Motor Corporation and Toyota Central R&D Labs, Inc., to evaluate thoracic injury risk across various impact locations and incident angles for two types of KIPs: the flash-ball and the 40 mm sponge round projectiles. The viscous criterion (VCmax) was used to assess the injury risk considering a threshold of 0.8 m/s, which represents a 50% probability of sustaining a thoracic injury of abbreviated injury scale (AIS) 2 or 3. Simulations demonstrated that impact location and incident angle influence injury severity, with near-perpendicular impacts yielding the highest VCmax values. Results indicated that due to its more concentrated frontal profile, the sponge round projectile transfers approximately 77% of its initial kinetic energy to the body, whereas the flash-ball projectile distributes force more widely, transferring 60% of its initial kinetic energy. Critically, both projectiles approached or exceeded the 0.8 m/s VCmax injury threshold at the manufacturer's recommended minimum firing distances, especially when impacting regions directly over the heart and lungs. In particular, the sponge round projectile presented up to an 80% probability of AIS 2–3 injury under standard operational conditions. These findings suggest that current KIP designs and thoracic targeting protocols may pose a substantial risk of severe injury, highlighting the urgent need for safer projectile designs and improved training to minimize unintended fatalities during deployment.

Reliable Benchmarking of Breast Ultrasound Lesion Classification Requires Patient-Level Validation

2026-08-23T08:03:17Z

Reliable Benchmarking of Breast Ultrasound Lesion Classification Requires Patient-Level Validation Wang, Lulu Abstract. Breast ultrasound is widely used for lesion characterization, yet reported deep-learning performance varies substantially with dataset composition, preprocessing, and validation design. A major source of bias arises when patient-level separation is not enforced, allowing correlated images from the same subject to inflate performance estimates. This study presents a leakage-aware benchmark of convolutional neural networks (CNNs), a Vision Transformer (ViT), and a CNN–transformer late-fusion configuration for benign-versus-malignant breast ultrasound classification on the BUS-UCLM dataset. After exclusion of normal-category images, the final cohort comprised 264 images from 36 patients, including 174 benign and 90 malignant images. Seven CNN-family models, one ViT baseline, and one ResNet18–ViT probability-level late-fusion configuration were evaluated using strict patient-level fivefold cross-validation. Additional analyses included fusion ablation, paired Wilcoxon signed-rank testing, gradient-weighted class activation mapping (Grad-CAM) visualization, and an image-level leakage demonstration. Under strict patient-level evaluation, performance was moderate across all models. GoogLeNet achieved the highest mean accuracy (61.48%), InceptionV3 achieved the highest mean macro-F1 (59.24%), and ResNet50 achieved the highest mean area under the receiver operating characteristic curve (AUC) (0.6699), whereas the standalone ViT showed weaker overall discrimination. The late-fusion configuration remained competitive in threshold-dependent metrics but did not surpass the strongest CNN baselines in AUC. Overall, no architecture demonstrated a clear advantage across both threshold-dependent and threshold-independent metrics. By contrast, image-level splitting substantially inflated apparent performance, underscoring the importance of rigorous patient-level separation for credible benchmarking in breast ultrasound classification.

Epileptic Seizure Detection Based on Convolutional Self-Attention Adaptive Dimensionality Expansion Network Using EEG Signals

2026-08-23T08:03:10Z

Epileptic Seizure Detection Based on Convolutional Self-Attention Adaptive Dimensionality Expansion Network Using EEG Signals Long, Xingyu Abstract. Traditional epileptic seizure detection methods suffer from poor interpretability, excessive parameters, and fixed parameters in time-domain dimensionality expansion. To address these, this study proposes a convolutional self-attention adaptive dimensionality expansion network (CSADI-Net), integrating convolutional self-attention and adaptive dimensionality expansion. Convolutional self-attention uses convolutional layers to generate Q (query, representing task-related attention cues), K (key, representing inherent signal characteristics), and V (value, representing input data), reducing trainable parameters (TP). Adaptive dimensionality expansion combines with network training for parameter adjustment. Class activation heatmaps enable visual interpretability. Validated on children's hospital Boston and the Massachusetts institute of technology (CHB-MIT) (Accuracy:98.87%, F1:98.49%) and temple university hospital (TUH) (Accuracy:98.26%, F1:98.13%) datasets, it outperforms CNN, CNN-LSTM, and linear self-attention Transformer. With high accuracy, antinoise ability, and interpretability, it provides a new perspective for seizure detection.