Ahmad P. Tafti: Explainable AI in Medical Image Analysis: Lessons Learned in TJA Research скачать в хорошем качестве

Ahmad P. Tafti: Explainable AI in Medical Image Analysis: Lessons Learned in TJA Research

The talk given by Ahmad P. Tafti at KUIS AI Talks on May 12 in 2022. Title: Explainable AI in Medical Image Analysis; Lessons Learned in Total Joint Arthroplasty (TJA) Research Abstract: Orthopedic surgical procedures, and particularly total knee/hip arthroplasty (TKA/THA), are the most common and fastest growing surgeries in the United States. Almost 1.3 million TJA procedures occur on a yearly basis and more than 7 million Americans are currently living with artificial knee and/or hip joints. The widespread adoption of x-ray radiography and their availability at low cost, make them the principal method in assessing TJA and subtle TJA complications, such as osteolysis, implant loosening or infection over time, enabling surgeons to rule out complications and possible needs for revision surgeries. Rapid yet, with the growing number of TJA patients, the routine clinical and radiograph follow-up remain a daunting task for most orthopedic centers. It becomes an overwhelming amount of work, on a human scale, when we consider a radiologist or surgeon presented with the vast number of medical images daily. Smart computational strategies, such as explainable artificial intelligence and deep learning methods are thus required to analyze arthroplasty radiographs automatically and objectively, enabling both naive and experienced practitioners to perform radiographic follow-up with greater ease and speed, providing them with better explainability and interpretability in AI models. In this talk, we will be discussing the effectiveness of explainable AI methods to advance TJA research. We, together, will explore what explainable AI components do in TJA research and how. Short Bio: Dr. Tafti is an Assistant Professor of Computer Science at the University of Southern Maine, where he is leading the USM HexAI Research Laboratory. He is also an advisory board member in Stanford Deep Data Research Computing Center within the School of Medicine at Stanford University. Dr. Tafti received his PhD in computer science with an emphasis on artificial intelligence and 3D computer vision. He is passionate about AI and its applications in healthcare. Dr. Tafti is the 2021 SiiM Imaging Informatics Innovator awardee, Mayo Clinic Transform the Practice awardee, an NVIDIA GPU awardee, and GE Healthcare Honorable Mention awardee. To date, he has authored 45+ peer-reviewed publications. Dr. Tafti has organized numerous workshops and tutorials on intelligent health systems and has served on the program committee of 15+ conferences, symposiums, and journals in AI and Digital Health Sciences.