Deep Learning-Based Uroflowmetry Curve Analysis Improves the Noninvasive Diagnosis of Lower Urinary Tract Symptoms
Purpose: This study aimed to evaluate the performance of an artificial intelligence (AI)-based analysis of uroflowmetry (UFM) curve images, enhanced with customized preprocessing techniques, to improve diagnostic accuracy for bladder outlet obstruction (BOO) and detrusor underactivity (DUA). Methods...
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| Published in: | International neurourology journal pp. 73 - 82 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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대한배뇨장애요실금학회
01.11.2025
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| ISSN: | 2093-4777, 2093-6931 |
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| Abstract | Purpose: This study aimed to evaluate the performance of an artificial intelligence (AI)-based analysis of uroflowmetry (UFM) curve images, enhanced with customized preprocessing techniques, to improve diagnostic accuracy for bladder outlet obstruction (BOO) and detrusor underactivity (DUA).
Methods: We retrospectively analyzed 2,579 UFM curve images from patients who underwent urodynamic study (UDS), including 725 normal and 1,854 abnormal cases (736 BOO and 1,387 DUA). A VGG16 convolutional neural network model was developed to perform 3 binary classification tasks: normal versus abnormal, BOO versus non-BOO, and DUA versus non DUA. To improve model performance, we implemented a preprocessing pipeline consisting of denoising, cropping, axis scaling, and color-coding of clinical parameters such as voided volume and postvoid residual volume (PVR). Model performance was evaluated using 5-fold stratified cross-validation and the area under the receiver operating characteristic curve (AUROC).
Results: Abnormal cases demonstrated a lower median maximum flow rate (8.9 mL/sec vs. 14.8 mL/sec), higher PVR (60.0 mL vs. 20.0 mL), and lower voiding efficiency (78.5% vs. 92.5%) than normal cases. Within the abnormal group, the BOO subgroup showed a higher PVR (80.0 mL) than the non-BOO subgroup (30.0 mL). After applying the preprocessing pipeline, model performance improved, with AUROC increasing from 0.807±0.024 to 0.827±0.016 for normal vs. abnormal classification, from 0.749±0.019 to 0.773±0.034 for BOO classification, and from 0.693±0.016 to 0.709±0.031 for DUA classification.
Conclusions: AI-based analysis of UFM curve images, enhanced through customized preprocessing, improved diagnostic accuracy in patients with lower urinary tract symptoms, effectively identifying BOO and DUA. This noninvasive method may serve as an adjunct or screening tool to reduce reliance on invasive UDS. KCI Citation Count: 0 |
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| AbstractList | Purpose: This study aimed to evaluate the performance of an artificial intelligence (AI)-based analysis of uroflowmetry (UFM) curve images, enhanced with customized preprocessing techniques, to improve diagnostic accuracy for bladder outlet obstruction (BOO) and detrusor underactivity (DUA).
Methods: We retrospectively analyzed 2,579 UFM curve images from patients who underwent urodynamic study (UDS), including 725 normal and 1,854 abnormal cases (736 BOO and 1,387 DUA). A VGG16 convolutional neural network model was developed to perform 3 binary classification tasks: normal versus abnormal, BOO versus non-BOO, and DUA versus non DUA. To improve model performance, we implemented a preprocessing pipeline consisting of denoising, cropping, axis scaling, and color-coding of clinical parameters such as voided volume and postvoid residual volume (PVR). Model performance was evaluated using 5-fold stratified cross-validation and the area under the receiver operating characteristic curve (AUROC).
Results: Abnormal cases demonstrated a lower median maximum flow rate (8.9 mL/sec vs. 14.8 mL/sec), higher PVR (60.0 mL vs. 20.0 mL), and lower voiding efficiency (78.5% vs. 92.5%) than normal cases. Within the abnormal group, the BOO subgroup showed a higher PVR (80.0 mL) than the non-BOO subgroup (30.0 mL). After applying the preprocessing pipeline, model performance improved, with AUROC increasing from 0.807±0.024 to 0.827±0.016 for normal vs. abnormal classification, from 0.749±0.019 to 0.773±0.034 for BOO classification, and from 0.693±0.016 to 0.709±0.031 for DUA classification.
Conclusions: AI-based analysis of UFM curve images, enhanced through customized preprocessing, improved diagnostic accuracy in patients with lower urinary tract symptoms, effectively identifying BOO and DUA. This noninvasive method may serve as an adjunct or screening tool to reduce reliance on invasive UDS. KCI Citation Count: 0 |
| Author | Kwang Jin Ko Jong Hoon Lee Myung Jin Chung Chung Un Lee Yungon Lee Deok-Hyun Han Jung Hyun Kim |
| Author_xml | – sequence: 1 fullname: Jong Hoon Lee organization: (Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea) – sequence: 2 fullname: Yungon Lee organization: (Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea) – sequence: 3 fullname: Kwang Jin Ko organization: (Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea) – sequence: 4 fullname: Myung Jin Chung organization: (Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea) – sequence: 5 fullname: Chung Un Lee organization: (Department of Urology, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine, Gwangmyeong, Korea) – sequence: 6 fullname: Jung Hyun Kim organization: (Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea) – sequence: 7 fullname: Deok-Hyun Han organization: (Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea) |
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| Title | Deep Learning-Based Uroflowmetry Curve Analysis Improves the Noninvasive Diagnosis of Lower Urinary Tract Symptoms |
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