Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow.

BACKGROUND: Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with conventional uroflowmetry and show promise as a convenient home-based alternative for the monitoring of urinary flows. OBJECTIVE: To evaluate the ability of a sound-based deep learning algorithm (Audioflow) to predict uroflowmetry parameters and identify abnormal urinary flow patterns. DESIGN, SETTING, AND PARTICIPANTS: In this prospective open-label study, 534 male participants recruited at Singapore General Hospital between December 1, 2017 and July 1, 2019 voided into a uroflowmetry machine, and voiding acoustics were recorded using a smartphone in close proximity. The Audioflow algorithm consisted of two models-the first model for the prediction of flow parameters including maximum flow rate (Qmax), average flow rate (Qave), and voided volume (VV) was trained and validated using leave-one-out cross-validation procedures; the second model for discrimination of normal and abnormal urinary flows was trained based on a reference standard created by three senior urologists. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Lin's correlation coefficient was used to evaluate the agreement between Audioflow predictions and conventional uroflowmetry for Qmax, Qave, and VV. Accuracy of the Audioflow algorithm in the identification of abnormal urinary flows was assessed with sensitivity analyses and the area under the receiver operating curve (AUC); this algorithm was compared with an external panel of graders comprising six urology residents/general practitioners who separately graded flow patterns in the validation dataset. RESULTS AND LIMITATIONS: A total of 331 patients were included for analysis. Agreement between Audioflow and conventional uroflowmetry for Qmax, Qave, and VV was 0.77 (95% confidence interval [CI], 0.72-0.80), 0.85 (95% CI, 0.82-0.88) and 0.84 (95% CI, 0.80-0.87), respectively. For the identification of abnormal flows, Audioflow achieved a high rate of agreement of 83.8% (95% CI, 77.5-90.1%) with the reference standard, and was comparable with an external panel of six residents/general practitioners. AUC was 0.892 (95% CI, 0.834-0.951), with high sensitivity of 87.3% (95% CI, 76.8-93.7%) and specificity of 77.5% (95% CI, 61.1-88.6%). CONCLUSIONS: The results of this study suggest that a deep learning algorithm can predict uroflowmetry parameters and identify abnormal urinary voids based on voiding sounds, and shows promise as a simple home-based alternative to uroflowmetry in the management of patients with LUTS. PATIENT SUMMARY: In this study, we trained a deep learning-based algorithm to measure urinary flow rates and identify abnormal flow patterns based on voiding sounds. This may provide a convenient, home-based alternative to conventional uroflowmetry for the assessment and monitoring of patients with lower urinary tract symptoms.

GABAergic neurons of the medial septum play a nodal role in facilitation of nociception-induced affect.

The present study explored the functional details of the influence of medial septal region (MSDB) on spectrum of nociceptive behaviours by manipulating intraseptal GABAergic mechanisms. Results showed that formalin-induced acute nociception was not affected by intraseptal microinjection of bicuculline, a GABAA receptor antagonist, or on selective lesion of septal GABAergic neurons. Indeed, the acute nociceptive responses were dissociated from the regulation of sensorimotor behaviour and generation of theta-rhythm by the GABAergic mechanisms in MSDB. The GABAergic lesion attenuated formalin-induced unconditioned cellular response in the anterior cingulate cortex (ACC) and blocked formalin-induced conditioned place avoidance (F-CPA), and as well as the contextual fear induced on conditioning with brief footshock. The effects of lesion on nociceptive-conditioned cellular responses were, however, variable. Interestingly, the lesion attenuated the conditioned representation of experimental context in dorsal hippocampus field CA1 in the F-CPA task. Collectively, the preceding suggests that the MSDB is a nodal centre wherein the GABAergic neurons mediate nociceptive affect-motivation by regulating cellular mechanisms in ACC that confer an aversive value to the noxious stimulus. Further, in conjunction with a modulatory influence on hippocampal contextual processing, MSDB may integrate affect with context as part of associative learning in the F-CPA task.