Hamstring Injuries (HSIs) are common among athletes and necessitate extended rehabilitation before Return to Sport (RTS). Post-injury, athletes undergo physical examinations, which often fall short in assessing injury severity or guiding rehabilitation. Therefore, imaging techniques such as Magnetic Resonance Imaging (MRI) are used to evaluate the injury more comprehensively, aiding in the assessment of optimal rehabilitation and RTS timelines. Given the significant impact of HSIs on athletic careers, early prediction is essential. This article investigates the use of Electrical Impedance Tomography (EIT) for HSI prediction. EIT, a noninvasive method, involves injecting a current or voltage into the affected area to detect property changes, allowing for real-time monitoring and supporting its role in HSI prediction. A semi-synthetic dataset was created using MRI scans of patients with hamstring injuries. The dataset was developed by mapping the boundaries of the hamstring muscles (semimembranosus, semitendinosus, and biceps femoris) with Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS). EIDORS generated EIT voltage measurements by defining muscle boundaries and setting appropriate properties, forming the basis for the dataset. Machine Learning (ML) models were then employed to validate the dataset by distinguishing between injured and healthy hamstrings. The best-performing model, Random Forest (RF), achieved an accuracy of 98%, demonstrating the potential of EIT in predicting HSIs.

The authors have declared no competing interest.

This work was supported by the Lebanese American University.

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The study used openly available human data that were originally located at:https://www.youtube.com/@dr.gayfirstlookmri The rest of the data is generated sythetically.

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