{"id":"07328","slug":"ai-enabled-surgical-resource--07328","source":{"id":"07328","dataset":"techtransfer","title":"AI-Enabled Surgical Resource Management","description_":"<p>This innovation is an AI-powered surgical management system that integrates a surgeon-facing application with real-time computer vision to optimize instrument and supply utilization. By bridging the communication gap between surgeons and staff, it significantly reduces hospital waste and labor costs associated with unnecessary sterilization and unused disposables.</p><p><h2>Description</h2>The technology consists of two integrated components: an EMR-agnostic mobile and web application and an AI-enabled computer vision monitoring system. The application features a visual \"tray builder\" that allows surgeons to create, edit, and share case-specific instrument lists in real time, shifting the workflow from an \"Opt-In\" to an \"Opt-Out\" model for unnecessary items. This digital platform also utilizes \"Green Rankings\" and gamified leaderboards to encourage sustainable practices among surgical groups. \r\n\r\nThe second component utilizes ceiling or table-mounted cameras paired with a deep learning object detection pipeline (YOLO-based CNN). A unique aspect of this AI is its Scale-Noise Space Augmented Learning (SNSAL) strategy, which trains the system to recognize instruments accurately despite varied magnifications, orientations, and low-light conditions common in operating rooms. This AI tracks the actual usage and \"first use\" timestamp of every instrument, feeding data back into the application to automatically refine future tray configurations and generate actionable waste reduction reports.</p><p><h2>Applications</h2>- Real-time management and optimization of surgical instrument and supply trays tailored per procedure.\r<br>- Reduction of operating room waste by identifying underutilized or unnecessary items through usage analytics.\r<br>- Enhancement of surgeon engagement in supply chain and sustainability efforts via interactive platform features and gamified feedback.\r<br>- Data-driven inventory management optimizing procurement and sterilization workflows.\r<br>- Benchmarking surgical practices across user groups to standardize and improve efficiency.\r<br>- Integration into hospital systems to enable seamless data exchange and operational alignment.\r<br>- Support for environmental sustainability initiatives by promoting resource-conscious surgical protocols.</p><p><h2>Advantages</h2>- Significant Waste Reduction: Curbs the one-third of hospital waste originating in the OR by ensuring only required disposable items are opened. \r<br>- Enhanced Operational Efficiency: Reduces energy and labor-intensive sterile processing by identifying instruments that are repeatedly sterilized but never used. \r<br>- Robust Detection Accuracy: Uses SNSAL technology to maintain high reliability in complex surgical environments with extreme scale variability and image noise. \r<br>- Improved Communication: Eliminates reliance on outdated paper-based lists and \"corporate memory\" by providing staff with real-time digital updates and item locations. \r<br>- Actionable Feedback Loops: Provides surgeons with direct data on their resource utilization compared to peers, fostering a culture of efficiency and sustainability.</p><p><h2>Invention Readiness</h2>The technology currently resides at an advanced prototype stage, with integrated hardware and software components validated through in-situ demonstrations within clinical-like settings. The system’s AI models have been trained and tested to reliably detect instrument usage, and initial user feedback has informed iterative refinements of the application interface and sustainability features. Further studies are warranted to validate long-term impact on operating room efficiency, waste reduction, and surgeon adoption patterns across diverse healthcare institutions. Additional optimization of interoperability and scalability parameters will support broader clinical implementation and regulatory readiness.</p><p><h2>IP Status</h2>Patent Pending</p><p></p>","tags":["Artificial intelligence (AI)","Surgery","Sustainability","Software"],"file_number":"07328","collections":[{"key":574,"name":"Healthcare AI"}],"meta_description":"AI-powered OR resource optimization merges real-time vision with surgeon app to cut waste, streamline trays, and boost sustainability.","image_url":"","apriori_judge_output":"{\"scores\":{\"novelty\":4.0,\"potential_impact\":4.0,\"readiness\":3.0,\"scalability\":3.0,\"timeliness\":4.0},\"weighted_score\":3.8,\"risks\":[\"AI-based instrument recognition may face regulatory/validation hurdles in clinical settings\",\"Data privacy and integration with hospital IT systems\",\"dependence on clinical workflow adoption and user training\",\"risk of over-reliance on automated tray configurations without human verification\"],\"one_sentence_take\":\"Strong novelty and timely impact with solid readiness, but regulatory validation and integration risks temper full-scale scalability.\"}","lead_inventor_name":"Joseph Rubin","lead_inventor_dept":"Med-Plastic Surgery","technology_type":"Digital Health","technology_subtype":"Other Digital Health","therapeutic_areas":[],"therapeutic_indications":[],"custom_tags":[],"all_tech_innovators":["Malke Asaad","María José Escobar Domingo, MD, MPH","Nickolas Gerald Littlefield","Hooman H. Rashidi","Joseph Peter Rubin","Solomon Gabriel Seckler MD","Ahmad Tafti"],"date_submitted":"2025-09-05"},"highlight":{},"matched_queries":null,"score":0.0}