Relationship Between Muscle and Fat Mass Using Deep-learning Approach in Preoperative Computed Tomography and a Neuromuscular Blocking Agent Dose Required for Maintaining Deep Neuromuscular Blockade in Patients Undergoing Gynecological Surgery

Deep neuromuscular blockade has benefits in various surgical procedures and reduces postoperative pain. While neuromuscular blockade dosages are often based on the patient's actual body weight, body composition can vary significantly depending on age, gender, and individual exercise levels. Therefore, uniformly estimating neuromuscular blockade dosages based on actual body weight can result in under- or over-administered neuromuscular blocking agents. Calculating neuromuscular blockade dosage based on actual body weight can be inaccurate, but it remains due to the difficulty of accurately quantifying human muscle and fat mass. Recent advances in image analysis techniques utilizing artificial intelligence models have led to the development of methods for quantifying muscle and fat mass from computed tomography (CT). Theoretically, a higher neuromuscular blockade dosage should be required as the muscle mass increases. This study aims to analyze the relationship between neuromuscular blocking agent dosage, onset time, profound blockade time, and recovery time, and muscle mass and fat mass calculated from preoperative CT scans.