Background: This study aims to compare the analgesic efficacy and procedural efficiency of pectoral (PECS II) blocks performed using artificial intelligence (AI)-integrated ultrasonography (USG) versus conventional USG in patients undergoing modified radical mastectomy (MRM). Patients and Methods: Between November 2021 and March 2023, a total of 70 female patients scheduled for unilateral MRM under general anesthesia were included in this randomized study. The patients were randomly allocated into two groups: USG group (n = 35) and AI-USG group (n = 35). A fourth-year anesthesiology resident performed the PECS II blocks under the supervision of a senior anesthesiologist. The primary outcome was the postoperative pain score as assessed by Visual Analog Scale (VAS) at 12 hours. Secondary outcomes included pain scores at other postoperative time points, total opioid consumption, time first to rescue analgesia request within 24 hours, and the resident’s skill development at the end of the study. Results: The mean age was 55.3±11.4 (range, 35 to 75) years. Intraoperative remifentanil consumption was higher in USG group than in AI-USG group; however, the difference was not statistically significant ( p > 0.05). The durations of anesthesia and surgery were shorter in AI-USG group ( p = 0.005 and p = 0.008, respectively). A comparison of local anesthetic injection times between the first 35 and the last 35 patients revealed a statistically significant decrease in the USG group (4.0 min vs. 3.0 min, p = 0.014). The VAS pain scores in the post-anesthesia care unit were initially higher in the AI-USG group ( p = 0.05); however, at 12 and 24 postoperative hours, VAS scores were significantly lower than those in the USG group ( p = 0.005 and p 0.05). Surgeon satisfaction scores were lower in the AI-USG group ( p = 0.037). Conclusion: Our study results suggest that AI-enhanced USG guidance is associated with improved analgesic outcomes and may offer clinical and educational advantages in the performance of PECS II blocks, particularly for residents in training. The integration of AI into routine USG-guided regional anesthesia practice holds promise for improving procedural consistency and supporting novice practitioners
Yazar et al. (Wed,) studied this question.