Advancing Endoscopic Cardiac Surgery through an AI-Enabled Guided Prediction System
In plain English
AI plain-English summaryFewer than one in five heart surgeries in the UK currently avoids splitting the breastbone. This project builds an AI system that reads CT scans before surgery, then generates a detailed 3D map of a patient’s heart and surrounding structures, so surgeons can plan and perform endoscopic cardiac procedures through small incisions instead of opening the chest. The problem is practical and widespread. Open-heart surgery requires a sternotomy—cutting through the breastbone—which causes significant pain, long recovery times, and risk of complications. Endoscopic approaches exist but are technically demanding and used in less than 20% of UK cases. Surgeons lack a reliable, automated tool to visualise critical anatomy and plan the safest route for instruments through the chest wall. If the system works, it could shift the default approach for many cardiac procedures. More surgeons would adopt endoscopic methods, leading to smaller scars, less postoperative pain, shorter hospital stays, and lower costs for the NHS. The technology integrates directly into existing surgical planning workflows, so adoption would not require a complete overhaul of hospital processes. The impact is on surgical practice itself—making a safer, less invasive option the routine choice rather than the exception.
View original technical description
View the original record at the funder ↗
Related Research
Grants with similar aims, by meaning.
Original classification
Collaborative R&DPlain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research. Is something wrong? Let us know