Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry

• Published in: Investigative and clinical urology Vol. 63; no. 3; pp. 301 - 308
• Main Authors: Bang, Seokhwan, Tukhtaev, Sokhib, Ko, Kwang Jin, Han, Deok Hyun, Baek, Minki, Jeon, Hwang Gyun, Cho, Baek Hwan, Lee, Kyu-Sung
• Format: Journal Article
• Language: English
• Published: Korea (South) The Korean Urological Association 01.05.2022; Korean Urological Association; 대한비뇨의학회
• Subjects: artificial intelligence; bladder outlet obstruction; Deep Learning; detrusor underactivity; Feasibility Studies; Female; Humans; lower urinary tract symptoms; Lower Urinary Tract Symptoms - diagnosis; Male; Original; Urinary Bladder; Urinary Bladder Neck Obstruction - diagnosis; Urodynamics; 비뇨기과학; Detrusor underactivity; Artificial intelligence; Lower urinary tract symptoms; Bladder outlet obstruction
• ISSN: 2466-0493; 2466-054X; 2466-054X
• DOI: 10.4111/icu.20210434

To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In this study, we used deep learning to analyze simple uroflowmetry. We performed a retrospective review of 4,835 male patients aged ≥40 years who underwent a urodynamic study at a single center. We excluded patients with a disease or a history of surgery that could affect LUTS. A total of 1,792 patients were included in the study. We extracted a simple uroflowmetry graph automatically using the ABBYY Flexicapture image capture program (ABBYY, Moscow, Russia). We applied a convolutional neural network (CNN), a deep learning method to predict DUA and BOO. A 5-fold cross-validation average value of the area under the receiver operating characteristic (AUROC) curve was chosen as an evaluation metric. When it comes to binary classification, this metric provides a richer measure of classification performance. Additionally, we provided the corresponding average precision-recall (PR) curves. Among the 1,792 patients, 482 (26.90%) had BOO, and 893 (49.83%) had DUA. The average AUROC scores of DUA and BOO, which were measured using 5-fold cross-validation, were 73.30% (mean average precision [mAP]=0.70) and 72.23% (mAP=0.45), respectively. Our study suggests that it is possible to differentiate DUA from non-DUA and BOO from non-BOO using a simple uroflowmetry graph with a fine-tuned VGG16, which is a well-known CNN model.