Kevin T Hobbs1, Nathaniel Choe2, Leonid I Aksenov1, Lourdes Reyes3, Wilkins Aquino3, Jonathan C Routh1, James A Hokanson4. 1. Division of Urologic Surgery, Duke University Medical Center, Durham, NC. 2. Department of Electrical and Computer Engineering, Duke University, Durham, NC. 3. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC. 4. Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI. Electronic address: jhokanson@mcw.edu.
Abstract
OBJECTIVE: To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation. MATERIALS AND METHODS: Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity. RESULTS: Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%). CONCLUSION: We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.
OBJECTIVE: To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation. MATERIALS AND METHODS: Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity. RESULTS: Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%). CONCLUSION: We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.
Authors: Stuart B Bauer; Paul F Austin; Yazan F Rawashdeh; Tom P de Jong; Israel Franco; Charlotte Siggard; Troels Munch Jorgensen Journal: Neurourol Urodyn Date: 2012-04-24 Impact factor: 2.696
Authors: Tara L Frenkl; Radha Railkar; John Palcza; Boyd B Scott; Achilles Alon; Stuart Green; Werner Schaefer Journal: Neurourol Urodyn Date: 2011-06-14 Impact factor: 2.696
Authors: Anne G Dudley; Mark C Adams; John W Brock; Douglass B Clayton; David B Joseph; Chester J Koh; Paul A Merguerian; John C Pope; Jonathan C Routh; John C Thomas; Duong D Tu; M Chad Wallis; John S Wiener; Elizabeth B Yerkes; Chelsea J Lauderdale; Chevis N Shannon; Stacy T Tanaka Journal: J Urol Date: 2017-12-29 Impact factor: 7.450
Authors: Elizabeth B Yerkes; Earl Y Cheng; John S Wiener; J Christopher Austin; Duong D Tu; David B Joseph; Jonathan C Routh; Stacy T Tanaka Journal: J Pediatr Urol Date: 2021-05-11 Impact factor: 1.830