I work with two main data streams—high-resolution imaging and electrophysiology (EP) mapping—and need to push my abnormality-detection pipeline further. On the imaging side I already extract dermatology and cardiovascular markers; now I want the same level of insight from EP maps so that arrhythmias and scar tissue stand out as clearly as a lesion on an MRI. Here is what I’m looking for: • A unified workflow that ingests both imaging files and raw EP mapping exports, cleans the inevitable clinical noise, and outputs structured features ready for analysis. • Robust models (machine learning, deep learning, plus supporting statistical tests) that flag arrhythmias and scar tissue with high sensitivity and clear explainability plots. • Side-by-side validation: cross-validation inside each modality and a comparative report that shows how well the two data sources agree on the same patient cases. • Lightweight documentation so I can plug new patient data in without touching the code. I’m comfortable reviewing Python, PyTorch or TensorFlow notebooks, but feel free to suggest other proven tools if they shorten turnaround or improve accuracy. Once I see your initial prototype on a small de-identified dataset, we can iterate on feature engineering, add broader ECG segments, or even explore conduction-delay detection in a later phase. If translating proven imaging techniques to EP mapping and wrapping them in a clean, clinician-friendly package sounds exciting, let’s talk timelines and next steps.