Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem

Blum, E; Porras, Antonio R.; Biggs, Elijah; Tabrizi, Pooneh R.; Sussman, Rachael D.; Sprague, Bruce M.; Shalaby‐Rana, Eglal; Majd, Massoud; Pohl, Hans G.; Linguraru, Marius George

doi:10.1016/j.juro.2017.09.147

Cited by 38 publications

(16 citation statements)

References 15 publications

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“…Blum et al. trained an ML model using renogram features to automatically detect hydronephrosis. The study showed that the ML model significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared with looking only at half‐time and 30‐min clearance.…”

Section: Application Of Ai In Urologymentioning

confidence: 99%

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Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. Materials and methods A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded. Results Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. Conclusion AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

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Section: Application Of Ai In Urologymentioning

confidence: 99%

“…Imaging radiomic features and AI algorithms have also been applied to detect clinically significant hydronephrosis or urinary reflex. Blum et al [18] trained an ML model using renogram features to automatically detect hydronephrosis.…”

Section: Hydronephrosis/urinary Refluxmentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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“…MAG3 uptake and drainage were identified and defined by O'Reilly et al [20], usually, the time consumed for clearance of 50% (t½) of the garnered radionuclide is <10 min, whereas a t½ of >20 min is indicative, but not diagnostic, of obstruction. Blum et al [21] studied renograms of 55 patients and proposed that better (t½) has an accuracy of 83% when used for 24.5 minutes. A study published on adult laparoscopic pyeloplasty reported t½ near 14 [17], whereas the present study found t½ up to 7.85 among the laparoscopic group and 9 in the open pyeloplasty group at one year.…”

Section: Discussionmentioning

confidence: 99%

Laparoscopic Versus Open Pyeloplasty for Primary Pelvic Ureteric Junction Obstruction: A Prospective Single Centre Study

Rehman

Umair

Hussain

et al. 2020

Cureus

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The aim of the study was to compare the clinical and patient-reported outcomes among open pyeloplasty (OP) and laparoscopic pyeloplasty (LP) patients. Materials and methods This was a prospective single centre, case-cohort study conducted in a tertiary care hospital with 62 patients. In both techniques, dismembered Anderson-Hynes pyeloplasty were undertaken. Post-operatively patients underwent visual analogue scale (VAS) assessment for pain, days to ambulation and comparison of the short-and long-term outcomes of the two procedures. Results There was no difference in the physical and functional outcomes between the two surgical approaches at 12 months period after surgery. However, patients in the laparoscopic group did report a higher rate of satisfaction at six weeks and six months' postoperatively. Likewise, patients in LP experienced less pain during the postoperative period (p-value <0.001), with decreased analgesic requirements. This translated into an early patient ambulation in the laparoscopic group (p-value <0.001), and a shorter hospital stay for the LP group (p-value <0.001). Moreover, follow-up ultrasound showed equal improvement of hydronephrosis among the two groups. Conclusion Laparoscopic and open pyeloplasty are equally effective in treating pelvic ureteric junction obstruction (PUJO), with comparable patient-reported outcomes at 12-month follow-up. However, the laparoscopic technique merits over open surgery with faster rehabilitation, a decreased postoperative pain experience and shorter hospital stay.

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“…The authors showed that SVM gives better results (AUC greater than 0.9) than the other tested methods. Blum et al (2018) used SVM on a data set of diuresis renogram recorded on 55 patients to predict uteropelvic junction obstruction.…”

Section: Re Vie W On Machine Le Arning Appli C Ati On S For Pred I mentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem

Cited by 38 publications

References 15 publications

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Laparoscopic Versus Open Pyeloplasty for Primary Pelvic Ureteric Junction Obstruction: A Prospective Single Centre Study

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

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