Urologist validation of an artificial intelligence-based tool for automated estimation of penile curvature.

INTRODUCTION: Severity of penile curvature (PC) is commonly used to select the optimal surgical intervention for hypospadias, either alone or in conjunction with other phenotypic characteristics. Despite this, current literature on the accuracy and precision of different PC measurement techniques in hypospadias patients remains limited. PURPOSE: Assess the feasibility and validity of an artificial intelligence (AI)-based model for automatic measurement of PC. MATERIAL AND METHODS: Seven 3D-printed penile models with variable degrees of ventral PC were used to evaluate and compare interobserver agreement in estimation of penile curvatures using various measurement techniques (including visual inspection, goniometer, manual estimation via a mobile application, and an AI-based angle estimation app. In addition, each participant was required to complete a questionnaire about their background and experience. RESULTS: Thirty-five clinical practitioners participated in the study, including pediatric urologists, pediatric surgeons, and urologists. For each PC assessment method, time required, mean absolute error (MAE), and inter-rater agreement were assessed. For goniometer-based measurement, the lowest MAE achieved was derived from a model featuring 86° PC. When using either UVI (unaid visual inspection), mobile apps, or AI-based measurement, MAE was lowest when assessing a model with 88° PC, indicating that high-grade cases can be quantified more reliably. Indeed, MAE was highest when PC angle ranged between 40° and 58° for all the investigated measurement tools. In fact, among these methodologies, AI-based assessment achieved the lowest MAE and highest level of inter-class correlation, with an average measurement time of only 22 s. CONCLUSION: AI-based PC measurement models are more practical and consistent than the alternative curvature assessment tools already available. The AI method described in this study could help surgeons and hypospadiology researchers to measure PC more accurately.

Automated measurement of penile curvature using deep learning-based novel quantification method.

Objective: Develop a reliable, automated deep learning-based method for accurate measurement of penile curvature (PC) using 2-dimensional images. Materials and methods: A set of nine 3D-printed models was used to generate a batch of 913 images of penile curvature (PC) with varying configurations (curvature range 18° to 86°). The penile region was initially localized and cropped using a YOLOv5 model, after which the shaft area was extracted using a UNet-based segmentation model. The penile shaft was then divided into three distinct predefined regions: the distal zone, curvature zone, and proximal zone. To measure PC, we identified four distinct locations on the shaft that reflected the mid-axes of proximal and distal segments, then trained an HRNet model to predict these landmarks and calculate curvature angle in both the 3D-printed models and masked segmented images derived from these. Finally, the optimized HRNet model was applied to quantify PC in medical images of real human patients and the accuracy of this novel method was determined. Results: We obtained a mean absolute error (MAE) of angle measurement <5° for both penile model images and their derivative masks. For real patient images, AI prediction varied between 1.7° (for cases of ∼30° PC) and approximately 6° (for cases of 70° PC) compared with assessment by a clinical expert. Discussion: This study demonstrates a novel approach to the automated, accurate measurement of PC that could significantly improve patient assessment by surgeons and hypospadiology researchers. This method may overcome current limitations encountered when applying conventional methods of measuring arc-type PC.

Deep learning based automated quantification of urethral plate characteristics using the plate objective scoring tool (POST).

INTRODUCTION: The plate objective scoring tool (POST) was recently introduced as a reproducible and precise approach to quantifying urethral plate (UP) characteristics and guide to selecting particular surgical techniques. However, defining the landmarks mandatory for the POST score from captured images can potentially leads to variability. Although artificial intelligence (AI) is yet to be wholly accepted and explored in hypospadiology, it has certainly brought new possibilities to light. OBJECTIVES: To explore the capacity of deep learning algorithm to further streamline and optimize UP characteristics appraisal on 2D images using the POST, aiming to increase the objectivity and reproducibility of UP appraisal in hypospadias repair. METHODS: The five key POST landmarks were marked by specialists in a 691-image dataset of prepubertal boys undergoing primary hypospadias repair. This dataset was then used to develop and validate a deep learning-based landmark detection model. The proposed framework begins with glans localization and detection, where the input image is cropped using the predicted bounding box. Next, a deep convolutional neural network (CNN) architecture is used to predict the coordinates of the five POST landmarks. These predicted landmarks are then used to assess UP characteristics in distal hypospadias. RESULTS: The proposed model accurately localized the glans area, with a mean average precision (mAP) of 99.5% and an overall sensitivity of 99.1%. A normalized mean error (NME) of 0.07152 was achieved in predicting the coordinates of the landmarks, with a mean squared error (MSE) of 0.001 and a 2.5% failure rate at a threshold of 0.2 NME. DISCUSSION: Our results support the possibility of further standardizing UP assessment from captured hypospadias images, and the use of machine learning algorithms and image recognition shows that these novel artificial intelligence technologies are useful for scoring hypospadias. External validation can provide valuable information on the generalizability and reliability of deep learning algorithms, which can aid in assessments, decision-making and predictions for surgical outcomes. CONCLUSIONS: This deep learning application shows robustness and high precision in using POST to appraise UP characteristics. Further assessment using international multi-centre image-based databases is ongoing.

Automated quantification of penile curvature using artificial intelligence.

Objective: To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images. Materials and methods: Nine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC. Results: The proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles. Conclusions: Considering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.

On the intrinsic innervation of the epididymis of the camel (Camelus dromedarius).

The innervation of the camel epididymis was studied in 26 apparently healthy, sexually mature animals aged between 4 and 12 years. The material was collected during the different seasons of the year. Generally, five samples were taken from each epididymis. To demonstrate the general innervation pattern, immunohistochemical reactions to protein gene product-9.5, neurofilaments and neuron-specific enolase were used, in addition to acetylcholinesterase histochemistry. The nerve supply of the epididymis comes from two sources: (1) The majority of fibers come from the N. spermaticus inferior and accompany the deferent duct. (2) Another contribution stems from the N. spermaticus superior and enters the head region of the epididymis. From the exterior, the nerves penetrate the capsule of the organ to reach the interductular connective tissue. The terminal ramifications are observed directly within the wall of the duct and the wall of the epididymal arteries. The veins of the camel epididymis are not innervated. In the wall of the ductus epididymidis, the nerve fibers form plexuses at the subepithelial level and in the muscular coat. The amount of nerve fibers increases from the head to the tail, paralleling an increase in the intrinsic musculature. The intramural and interductular innervation of epididymal body and tail shows clear seasonal variations: More fibers and stronger reactions are observed during the winter season; the lowest density and the weakest reactions occur during the summer season. All epididymal nerves of the camel are unmyelinated. The majority of the intramural fibers and all in the arterial wall represent postjunctional sympathetic axons, but in the intramural plexuses of the duct a considerable number of cholinergic fibers are also present. Neuropeptide Y is the most frequent peptidergic transmitter and generally co-localized with dopamine-beta-hydroxylase in the sympathetic axons. Vasoactive intestinal polypeptide has a distribution similar to that of the cholinergic fibers. Calcitonin gene-related peptide-positive axons occur in moderate numbers, but never in the arterial innervation. Together with the relatively rare substance P-containing fibers, the calcitonin gene-related peptide-positive axons seem to represent the only sensory nerves in the camel epididymis.

Immunohistochemical investigations of the autonomous nerve distribution in the testis of the camel (Camelus dromedarius).

The distribution of autonomous nerves in the testis of the camel was studied by immunohistochemical methods. A total of 26 testes was collected during the different seasons of the year. As pan-neuronal markers, antibodies to protein gene product 9.5 and to neurofilaments are superior to antibodies against neuron-specific enolase and acetylcholinesterase histochemistry for the description of the nerves in the camel testis. Testicular nerves reach the camel testis by three access-routes as (1) funicular contribution, (2) mesorchial contribution and (3) as caudal contribution. The main target for testicular nerves is the arterial vascular tree of the organ, whereas all veins of testis and pampiniform plexus are devoid of any innervation in the camel. In the wall of the arteries, the nerves form a plexus at the media-adventitia border. The density of the arterial plexuses increases along the vascular tree: smaller septal and mediastinal arteries are better innervated than albugineal arteries and the latter better than the A. testicularis. The nerves in the septula testis, in the mediastinum and between the Leydig cells show clear seasonal changes, being particularly abundant in autumn and particularly scarce in spring. The nerves that reach the camel testis are unmyelinated and represent in the vast majority postjunctional sympathetic neurons. Cholinergic fibers are absent in the camel testis. Neuropeptide Y is the dominating peptidergic transmitter in the testicular nerves and colocalized with noradrenaline in the same axons. Vasoactive intestinal polypeptide-containing fibers reach the camel testis exclusively as parts of the caudal nervous contribution via the ligamentous bridge between testis and epididymal tail and are restricted to the caudal pole of the testis. Calcitonin gene-related peptide-positive axons are not frequent in the camel testis; nevertheless, they seem to be the most important sensory pathway of this organ.