Sylvain Bodard

Leo Milecki, Vicky Kalogeiton, Sylvain Bodard et al.

Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP -- Medical Images with clinical Prompts -- a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP.

Sylvain Bodard, Pierre Baudot, Benjamin Renoust et al.

Early detection of malignant lung nodules remains limited by reliance on size- and growth-based screening criteria, which can delay diagnosis. We present an integrated AI system that - unlike conventional CADe or CADx approaches - jointly performs nodule detection and malignancy assessment directly at the nodule level from low-dose CT scans within a unified aided decision framework. To address limitations in dataset scale and explainability, we designed an ensemble of shallow deep learning and feature-based specialized models, trained and evaluated on 25,709 scans with 69,449 annotated nodules, with external validation on an independent cohort. The system achieves an area under the receiver operating characteristic curve (AUC) of 0.98 internally and 0.945 on an independent cohort, outperforming radiologists and leading AI models (Sybil, Brock, Google, Kaggle). With a sensitivity of 99.3 percent at 0.5 false positives per scan, it addresses key barriers to AI adoption and demonstrates improved performance relative to both Lung-RADS size-based triage and European volume- and VDT-based screening criteria. The model outperforms radiologists across all nodule sizes and cancer stages - excelling in stage I cancers - and across all growth-based metrics, including volume-doubling time. It also surpasses radiologists by up to one year in diagnosing indeterminate and slow-growing nodules.