RadiologIA is a pedagogical interactive tool for learning how to analyse the anatomy of the thoracic cage on X-ray images. Leveraging a set of pretrained AI algorithms, this tool allows users (typically students in medical radiology) to assess their skills to identify anatomical regions of the chest. After interactively selecting the region they believe to be expected, their selection is evaluated using AI and a quantitative score as well as a qualitative visual feedback are given.

This project aims to create a simple and effective tool for highly accurate yield estimation for fruit growers based on smartphone images of apple trees combined with fruit diameter measurements and micro-climatic data. The tool will be integrated into the IoT platform developed by HEIG-VD as part of the project Novel. To reach the project objective this innovative approach will combine image analysis and machine learning techniques to automatically estimate the number of fruits on the one hand and a fruit growth model built upon the direct measurement of fruit size using connected dendrometers on the other. The existing IoT platform also continuously collects climate and soil moisture data, which will be integrated into the model for more accurate yield predictions. The mobile app, incorporating the capture of images, will be designed to be user-friendly, ensuring that farmers can easily adopt and use it in their daily routines.

The MREye-Track project aims to introduce a new approach that will address the limitations of current Magnetic Resonance Eye Imaging practice by employing machine learning techniques to raw MRI data (frequency domain) for the automatic identification of i) acquisitions affected by eye movements and ii) gaze direction, to reconstruct a high-quality anatomical image of the eye without additional equipment. By enabling the analysis of both eyes from the same scan and minimizing the need for hospital resources and examination time, MREye-Track will markedly improve the accessibility of ocular MRI.

This project aims to explore intelligent data analysis to uncover novel insights that could aid in enhancing and increasing the automation of PhenomX Health's recommendation engine, thereby enabling the reduction of manual effort, minimizing personal bias, and easing the capacity constraints as the user base expands.

Given the lack of understanding of the links between the factors of low back pain and its progression, the Digiback project aims to identify the different types of movement alterations in patients suffering from acute and chronic low back pain, and to study the relationships between movement alteration, pain intensity, and psychological factors.

Context-aware monitoring of patients with upper-limb neurological disorders in free-living conditions