Using wearable robotics to modulate step width in normal walking for enhanced mediolateral balance has not been demonstrated in the field. We designed a bilateral hip exoskeleton with admittance control to power hip abduction and adduction to modulate step width. Objective: As the first step to show its potential, the objective of this study was to investigate how human's step width reacted to hip exoskeleton's admittance control parameter changes during walking. Methods: Ten non-disabled individuals walked on a treadmill at a self-selected speed, while wearing our bilateral robotic hip exoskeleton. We used two equilibrium positions to define the direction of assistance. We studied the influence of multiple stiffness values in the admittance control on the participants’ step width, step length, and electromyographic (EMG) activity of the gluteus medius. Results: Step width were significantly modulated by the change of stiffness in exoskeleton control, while step length did not show significant changes. When the stiffness changed from zero to our studied stiffness values, the participants’ step width started to modulate immediately. Within 4 consecutive heel strikes right after a stiffness change, the step width showed a significant change. Interestingly, EMG activity of the gluteus medius did not change significantly regardless the applied stiffness and powered direction. Conclusion: Tuning of stiffness in admittance control of a hip exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus medius activity indicates that the increase in step width were mainly caused by the assistive torque applied by the exoskeleton. Significance: Our study results pave a new way for future design and control of wearable robotics in enhancing mediolateral walking balance for various rehabilitation applications.

The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.

Delays in the referral of patients with suspected metastatic bone disease (MBD) from radiology reports represent a critical challenge that can negatively impact patient outcomes. The conventional manual review process is often a significant bottleneck for triaging, leading to prolonged diagnostic timelines. We developed and validated SHIELD, an automated AI framework designed to accelerate and improve the accuracy of MBD referrals. We fine-tuned a RadBERT-RoBERTa model on a decade of radiology reports ( N = 245 patients) from two academic medical centers to classify reports into three tiers: “No Referral,” “Referral,” and “Referral/High Risk.” To ensure clinical utility and transparency, SHIELD incorporates a Large Language Model to generate natural-language explanations for its classifications designed to support clinical interpretability and auditability. SHIELD demonstrated high performance on a hold-out test set. It achieved 100% accuracy and an Area Under the Curve (AUC) of 1.00 in the primary binary task of distinguishing referral from non-referral cases. In the more granular three-class task, the model achieved an overall accuracy of 90%, with near-perfect performance in identifying “No Referral” reports (F1-score: 100%). Critically, the model operated in a clinically “fail-safe” manner, never misclassifying a high-risk case as requiring no referral. A retrospective timeline analysis revealed that SHIELD can reduce the referral period from a conventional average of 109.6 days to a computational time of 1–3 min. Proposed work provides high accuracy with a sophisticated explainability component using a large language model. Thus, SHIELD framework is a robust, explainable, and autonomous solution for triaging radiology reports. By drastically reducing administrative and diagnostic delays, it has the potential to significantly accelerate the clinical workflow, ensure timely specialist consultation, and ultimately improve the standard of care for patients with suspected MBD.

-Using wearable robotics to modulate step width in normal walking for enhanced mediolateral balance has not been demonstrated in the field. We designed a bilateral hip exoskeleton with admittance control to power hip abduction and adduction to modulate step width. Objective: As the first step to show its potential, the objective of this study was to investigate how human’s step width reacted to hip exoskeleton’s admittance control parameter changes during walking. Methods: Ten non-disabled individuals walked on a treadmill at a self-selected speed, while wearing our bilateral robotic hip exoskeleton. We used two equilibrium positions to define the direction of assistance. We studied the influence of multiple stiffness values in the admittance control on the participants’ step width, step length, and electromyographic (EMG) activity of the gluteus medius. Results: Step width were significantly modulated by the change of stiffness in exoskeleton control, while step length did not show significant changes. When the stiffness changed from zero to our studied stiffness values, the participants’ step width started to modulate immediately. Within 4 consecutive heel strikes right after a stiffness change, the step width showed a significant change. Interestingly, EMG activity of the gluteus medius did not change significantly regardless the applied stiffness and powered direction. Conclusion: Tuning of stiffness in admittance control of a hip exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus medius activity indicates that the increase in step width were mainly caused by the assistive torque applied by the exoskeleton. Significance: Our study results pave a new way for future design and control of wearable robotics in enhancing mediolateral walking balance for various rehabilitation applications.

Background/Objectives: Digital pathology models increasingly infer molecular phenotypes from hematoxylin and eosin whole-slide images (WSIs), but most assume that one slide adequately represents patient-level tumor biology. We quantified prediction instability caused by slide selection and evaluated multi-WSI aggregation. Methods: Six multiple-instance learning architectures were developed in task-specific subsets of TCGA-BRCA (source cohort: 1006 patients and 1065 WSIs) and independently evaluated in 122 patients (400 WSIs) from CPTAC-BRCA. The clinically realistic comparison was random one-slide-per-patient inference versus patient-level aggregation of all available WSIs. Label-conditioned best- and worst-slide analyses were used only as retrospective oracle bounds. The recurrence-risk endpoint was a research-derived 21-gene recurrence-score surrogate calculated from RNA sequencing rather than a clinically reported Oncotype DX result. Results: Random single-slide selection yielded AUCs of 0.77–0.86 for recurrence-risk prediction and 0.65–0.77 for HER2 status. Patient-level multi-WSI aggregation yielded AUCs of 0.81–0.89 and 0.71–0.80, respectively. The architecture-controlled AUC improvement from random selection to aggregation ranged from 0.026 to 0.041 for recurrence risk and from 0.036 to 0.073 for HER2. Aggregation also reduced Brier scores by 0.010–0.028 and 0.009–0.106, respectively. The label-conditioned oracle analyses demonstrated wider theoretical performance ranges of 0.46–0.97 and 0.40–0.92, respectively. Slide-selection sensitivity persisted after excluding tumor-evidence-negative and low-tumor-content WSIs. Conclusions: Slide selection is a material source of sampling-dependent prediction uncertainty. Patient-level multi-WSI aggregation mitigated selection-dependent degradation across architectures, although validation using routine institutional material and clinically reported molecular assays remains necessary.