Min Zhou

Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.

Using retrospective data from 100 patients who underwent transsphenoidal endoscopic pituitary tumor resection between 2016 and 2025, the study extracted 22 preoperative variables and developed and evaluated an artificial neural network and a random forest model against R², MAE, RMSE, and clinical accuracy thresholds of ±30 and ±45 minutes; the ANN outperformed random forest with a test-set MAE of 0.636 hours (about 38 minutes), 63.3% of predictions fell within ±30 minutes and 76.7% within ±45 minutes, and key predictors included tumor recurrence, distance from the third ventricle floor, and the cinch sign, which the authors present as objective evidence for nursing scheduling and operating room planning.