Anthony Fadel

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About

Anthony Fadel is a rising surgical outcomes researcher whose work sits at the intersection of urologic oncology, artificial intelligence, and precision medicine. His primary research focuses on leveraging machine learning to predict postoperative complications in patients undergoing radical cystectomy for bladder cancer. In his landmark 2024 study, Fadel developed and validated a novel AI algorithm that automatically extracts body composition metrics—including muscle and adipose tissue areas—from preoperative CT scans. This work demonstrated that sarcopenia and altered fat distribution are powerful, independent predictors of 90-day complications, offering a non-invasive tool to risk-stratify patients before major surgery. Beyond AI applications, Fadel has also made significant contributions to reconstructive urology, characterizing outcomes for both open and robotic uretero-enteric stricture repair in a large single-institution series. His early work has already garnered over 13 citations in under two years, signaling strong interest from the surgical community. By combining advanced computational methods with rigorous clinical data, Fadel is helping to usher in an era of truly personalized surgical care, where preoperative imaging can guide shared decision-making and optimize patient outcomes.