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 (<1 s)[1]. Grad-CAM visualization is a welcome addition, offering a first step toward interpretability for clinicians who are understandably cautious about “black box” outputs[1]. From a translation standpoint, the most immediate value may be standardization and triage – particularly in settings with variable urodynamic expertise. This aligns with contemporary guideline pathways that emphasize structured evaluation and risk stratification in NLUTD, where urodynamic findings can meaningfully alter management (e.g., escalating to therapies that protect the upper tracts)[4,5]. Important limitations remain before broad deployment. The dataset is single-center with fixed acquisition and screenshot conventions; robustness across vendors, display layouts, and image quality needs multicenter external validation[1]. Excluding men with organic obstruction (e.g., BPH/stricture) improves internal validity but narrows real-world applicability because mixed etiologies are common[1]. Finally, any diagnostic-assist tool should ultimately be judged not only by mAP/AUC but by clinical impact: whether it reduces inter-reader variability, shortens time-to-decision, and improves outcomes that matter in NLUTD (renal deterioration, infections, catheter dependence)[4,5]. Overall, Liu et al provide a pragmatic blueprint for AI-assisted videourodynamics in male NLUTD[1]. The next steps are clear: external validation, device-robustness testing, and prospective impact studies embedded within guideline-concordant care pathways.