Anthony Ta

Background/Objectives: Double-J stents (DJS) cause clinically significant lower urinary tract symptoms (LUTS) in 58–80% of patients following ureteroscopy (URS). The distal vesical coil is regarded as the primary anatomical source of trigonal irritation and detrusor instability. The single-J stent (SJS) eliminates this element while preserving the proximal renal pigtail for anti-migration anchorage. This study aimed to evaluate the safety and patient-reported symptom outcomes of a modified SJS compared with standard DJS following elective URS for ureteric stone disease. Methods: A prospective, single-arm, within-patient feasibility study was conducted in 28 adults undergoing elective URS at Austin Health, Melbourne, Australia (Human Research Ethics Committee (HREC)/25658/Austin-2025; NCT07535580). A modified SJS was fashioned from a commercially available 7Fr Bander™ ureteral diversion stent (Cook Medical, G14780) by trimming the shaft to 28 cm and affixing a 7 cm Prolene retrieval suture (Ethicon NW807) to the blunt distal end. The primary outcome was a cystoscopically confirmed SJS migration rate at removal (pre-specified feasibility threshold ≤5%). Secondary outcomes included change in Ureteral Stent Discomfort Test (USDT) total and domain scores (paired analysis, n = 24), adverse events, and patient preference. Results: The clinically significant migration rate was 0% (0/28); 27/28 stent tips were freely floating within the bladder, with one asymptomatic tip at the ureteric orifice at the scheduled removal visit. Mean total USDT score decreased from 39.92 ± 2.00 (DJS) to 30.42 ± 1.73 (SJS)—a mean absolute reduction of 9.50 points (23.8%; p < 0.001), a magnitude consistent with clinically relevant change, although a USDT-specific minimum clinically important difference has not been independently validated. All six USDT domains improved significantly (all p ≤ 0.014). No adverse events were recorded. All 28 patients (100%) reported they would choose the SJS again. Conclusions: The modified SJS demonstrated a favourable safety profile and was associated with lower patient-reported symptom scores than the preceding DJS across all USDT domains in this uncontrolled within-patient comparison. Because stent design, stone burden, and dwell time differed between the two assessment phases, these differences should be regarded as hypothesis-generating and provide a feasibility basis for a prospective randomised controlled trial evaluating the SJS following URS.

Introduction: Future evolutions of artificial intelligence (AI) will support autonomous surgery, conducted without the need for human decision making and implementation, but we have not yet achieved this level of technology. Presently, the predominant applications of AI in urological surgery are achieved using the tool of computer vision. This review aims to summarise potential intra-operative AI tools for urologists. Method: A systematic search was conducted through Scopus, PubMed, Embase, and Medline by two independent reviewers, with a third to resolve any conflicts. As a rule, only original articles describing the use or potential use of artificial intelligence intra-operatively in urologic surgery were included. A total of 60 articles were reviewed. Key content and findings: There is significant research investigating the ability to diagnose bladder tumours using AI assistance at the time of cystoscopy, with studies showing the ability to also grade tumour based on appearance and differentiate between carcinoma in situ and indeterminate lesions. With the aid of AI, kidney stones can accurately be identified and diagnosed morphologically intra-operatively. Various studies show the ability to overlay 2D and 3D anatomical models on a surgeon’s screen, as well as correctly identify important anatomical landmarks and surgical instruments, with AI support. All types of intra-operative data can be analysed with AI to assess surgeon performance, predict post-operative outcomes such as continence post prostatectomy, and recognise complications such as bleeding and ischemia. Conclusions: AI holds great potential for urologists during surgery to improve safety, diagnostic accuracy, identification of anatomical structures and surgical instruments, assessment of the surgeon for self-evaluation, and prediction of post-operative outcomes. Before the use of AI as an aid during surgery becomes standard practice, more prospective studies are needed to evaluate its real-world application, feasibility, and costs.