Letter to the Editor on “Research on the auxiliary diagnosis of male neurogenic lower urinary tract dysfunction based on a deep-learning algorithm”

To the Editor, Liu et al address a real bottleneck in neuro-urology: interpretation of video urodynamics in male neurogenic lower urinary tract dysfunction (NLUTD) is complex, time-consuming, and operator-dependent, with direct implications for risk stratification and renal safety[1]. This commentary was prepared in line with the TITAN 2025 Guidelines to promote transparent and responsible reporting[2]. A key strength is the authors’ pragmatic framing of “ground truth.” Rather than purely subjective labels, they anchor classification to International Continence Society-based pressure–flow indices derived from PdetQmax and Qmax (BOOI and BCI), which remain central to quantifying bladder outlet obstruction and detrusor contractility in men[3]. The model design is also clinically sensible: combining pressure–flow trace screenshots with voiding cystourethrography images mirrors how urodynamicists integrate functional curves with fluoroscopic anatomy during videourodynamics[1]. The study further benefits from temporal separation of cohorts (2009–2019 training/validation; 2020–2021 testing), reducing the optimism typically seen with random splits[1]. Reported performance is encouraging: the yolov10x model achieved high overall mAP for qualitative evaluation (BOO/contractility categories), while yolov5x performed best for obstruction localization with rapid inference (