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Niamh Belton

Student

Project Title

MRI Classification, Automatic Report Generation and Modelling of Sensor Data for Musculoskeletal Injury Management

Project Description

The main objective of this project is to develop machine learning techniques for musculoskeletal injury management. The themes of the project are computer vision, language in machine learning and machine learning for sequential data. The project will consist of two phases. The first phase will focus on medical image analysis of Magnetic Reasoning Imaging (MRI) in musculoskeletal regions such as the calf muscle. The aim of this retrospective analysis is to detect and classify injuries. The project may also extend to classifying injuries based on Diffusion Tensor Imaging (DTI). Applications for machine learning in medical image analysis extend beyond classification problems. Automatic generation of the radiologist report is a useful application of machine learning as analysing a medical image and writing a standardised report can be both time consuming and tedious. Many notable approaches have been developed in the field of automating the generation of radiology reports for X-rays of the chest. However, there is a sparsity of literature applying such techniques to 3D medical images such as MRIs. Phase one of the project will apply or further develop approaches to automate the radiologist reports for MRIs of musculoskeletal areas. The phase one model will be trained with multi and cross-modal inputs. Multiple images from an MRI from different angles will be input into the model, along with the radiologist report in order to train the model to generate the report. Structured data such as injury statistics and demographic data will also be used as input to aid the generation of the radiologist report. Methods of combining multi and cross modal input will be explored. The objective of phase two is to develop machine learning techniques for musculoskeletal injury rehabilitation. This will involve analysing sensor data to ensure physio-therapy or exercise is performed correctly. Electromyography (EMG) and/or accelerometry are some of the possible types of sensor data that will be used in phase two.