Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence
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Purpose: To develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI). Methods: A prospectively collected 1,574 UFM results (1,031 males, 543 females) with voided volume>150 mL was labelled as normal, borderline, or abnormal by 3 urologists. If the 3 experts disagreed, the majority decision was accepted. Abnormality was defined as a condition in which a urologist judges from the UFM results that further evaluation is required and that the patient should visit a urology clinic. To develop the optimal automatic interpretation system, we applied 4 ML algorithms and 2 deep learning (DL) algorithms. ML models were trained with all UFM parameters. DL models were trained to digitally analyze 2-dimensional images of UFM curves. Results: The automatic interpretation algorithm achieved a maximum accuracy of 88.9% in males and 90.8% in females when using 6 parameters: voided volume, maximum flow rate, time to maximal flow rate, average flow rate, flow time, and voiding time. In females, the DL models showed a dramatic improvement in accuracy over the other models, reaching 95.4% accuracy in the convolutional neural network model. The performance of the DL models in clinical discrimination was outstanding in both genders, with an area under the curve of up to 0.957 in males and 0.974 in females. Conclusions: We developed an automatic interpretation algorithm for UFM results by training AI models using 6 key parameters and the shape of the curve; this algorithm agreed closely with the decisions of urology specialists.
Abstract
Purpose: To develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI). Methods: A prospectively collected 1,574 UFM results (1,031 males, 543 females) with voided volume>150 mL was labelled as normal, borderline, or abnormal by 3 urologists. If the 3 experts disagreed, the majority decision was accepted. Abnormality was defined as a condition in which a urologist judges from the UFM results that further evaluation is required and that the patient should visit a urology clinic. To develop the optimal automatic interpretation system, we applied 4 ML algorithms and 2 deep learning (DL) algorithms. ML models were trained with all UFM parameters. DL models were trained to digitally analyze 2-dimensional images of UFM curves. Results: The automatic interpretation algorithm achieved a maximum accuracy of 88.9% in males and 90.8% in females when using 6 parameters: voided volume, maximum flow rate, time to maximal flow rate, average flow rate, flow time, and voiding time. In females, the DL models showed a dramatic improvement in accuracy over the other models, reaching 95.4% accuracy in the convolutional neural network model. The performance of the DL models in clinical discrimination was outstanding in both genders, with an area under the curve of up to 0.957 in males and 0.974 in females. Conclusions: We developed an automatic interpretation algorithm for UFM results by training AI models using 6 key parameters and the shape of the curve; this algorithm agreed closely with the decisions of urology specialists.
참고문헌 (26)
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