In transcatheter edge-to-edge repair (TEER), a leaking mitral valve is repaired through a catheter while the operator watches a live transoesophageal echo image. Every decision, where to place the clip, when to grasp the leaflets, whether to release, is made by eye, and there is not a single number on the screen. When we gave the same frame to four specialists they disagreed by two to four millimetres. That gap is why the learning curve is long, why outcomes vary between operators, and why many hospitals cannot start a programme.

AutoClip is a segmentation model that maps the valve and the clip on every ultrasound frame and turns each decision into a measurement: clip position on the papillary-muscle axis, insertion depth of each leaflet, and grasped leaflet length. The operator still decides; AutoClip gives a number where there was a guess. It is built as a device-agnostic layer that works with any clip and any echo vendor, and its intended use is to improve procedural outcomes: procedure time, clip malposition and re-intervention.

AutoClip was built by a cardiac surgeon with generative-AI coding tools, on a multi-centre dataset of about 2,000 procedures from four hospitals in Europe and China, with an outcome-linked registry so that every readout can be tested against results. It was selected as a Featured Innovation at TCT 2025 while still a prototype. Turicor is now building the real-time version and preparing clinical validation.