Pezhman Eghbalishamsabadi

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This thesis aimed to estimate the effect of demographic and biomechanical preoperative variables on total shoulder arthroplasty (TSA) outcomes. To achieve this, various biomechanical variables were calculated using a clinical database comprising normal and ...

Background: Static posterior subluxation of the humeral head (SPSH) results in glenohumeral osteoarthritis. Treatment strategies for SPSH with or without resulting osteoarthritis remain challenging. There is growing interest in evaluating the rotator cuff ...

Objectives: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis. Materials and Methods: First, we used dee ...

Energy harvesting in the railway industry has great potential for many applications. Energy harvesters can provide electrical power for track-side, on-board, and infrastructure instruments, including health monitoring sensors, signaling, switches, and safe ...

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we develope ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

Pezhman Eghbalishamsabadi

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Multimodal Prediction Framework for Total Shoulder Arthroplasty Outcomes: Integrating Morphological, Musculoskeletal, Finite Element, and Bayesian Causal Models

Three-dimensional evaluation of the transverse rotator cuff muscle's resultant force angle in relation to scapulohumeral subluxation and glenoid vault morphology in nonpathological shoulders

Automatic quantification of scapular and glenoid morphology from CT scans using deep learning

Sound and vibration energy harvesting for railway applications: A review on linear and nonlinear techniques

Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Glenohumeral joint force prediction with deep learning