Hypospadias-associated penile curvature assessment and management: A global survey of current practice.

INTRODUCTION: Our goal was to assess how surgical management of hypospadias-associated penile curvature (HAPC) varies across continents, focusing on factors that influence assessment and decision-making. METHODS: Members of the European Society of Pediatric Urology (ESPU), Society of Pediatric Urology (SPU), and Hypospadias International Society (HIS) participated in an anonymous, 34-question online survey addressing pre-, intra-, and postoperative elements of HAPC evaluation and management. A selection of intraoperative photos were included in the survey to investigate the prevailing surgical approaches and identify management patterns. RESULTS: Out of the 267 participants, 38.4% of them are located in Europe. Visual estimation was the predominant approach for evaluating HAPC, although being regarded as the least dependable compared to other techniques. Surgeons who performed more than 40 cases per year were more inclined to use goniometers and had varying degrees of HAPC that were considered acceptable without requiring any correction (P < .001). Out of 58% of respondents, a significant number reported regular utilization of artificial erection tests for all categories of hypospadias. Surgeons with fewer than 10 years of expertise commonly utilized erection test as part of their regular practice. A tourniquet was employed to maintain sufficient intra-corporeal pressure, by134 (50%). 116 participants (43%) inject Saline through the corpora cavernosa through the glans, while 150 (56%) administer saline from the lateral aspect. Moreover, the decision-making process differed based on the intraoperative picture scenarios of mild to moderate penile curvature during erection testing. Contrary to temperatures ranging from 25o to 35o, decision-making in cases with less severe degrees of HAPC was uncomplicated. CONCLUSION: This survey reveals a wide range of surgical practice patterns in the assessment and management of HAPC. To our knowledge, this global survey of HAPC practice is the largest to date and could aid in developing new guidelines in pediatric urology. These findings may also provide a foundation for future prospective multinational studies.

Trilayer composite scaffold for urethral reconstruction:in vitroevaluation of mechanical, biological, and angiogenic properties.

Regeneration of damaged urethral tissue remains a major challenge in the field of lower urinary tract reconstruction. To address this issue, various synthetic and natural biodegradable biomaterials are currently being explored for the fabrication of scaffolds that promote urethral regeneration and healing. In this study, we present an approach to fabricate a trilayer hybrid scaffold comprising a central layer of poly(lactic acid) (PLA) between two layers of chitosan. The chitosan/PLA/chitosan (CPC) scaffolds were fabricated by a sequential electrospinning process and their properties were evaluated for their suitability for urethral tissue engineering. The physical and biological properties of the CPC scaffolds were evaluated in comparison to electrospun PLA scaffolds and acellular dermis (Alloderm) as controls for a synthetic and a natural scaffold, respectively. Compared to the controls, the CPC scaffolds exhibited higher elastic modulus and ultimate tensile strength, while maintaining extensibility and suture retention strength appropriate for clinical use. The CPC scaffolds displayed significant hydrophilicity, which was associated with a higher water absorption capacity of the chitosan nanofibres. The degradation products of the CPC scaffolds did not exhibit cytotoxicity and promoted wound closure by fibroblastsin vitro. In addition, CPC scaffolds showed increased growth of smooth muscle cells, an essential component for functional regeneration of urethral tissue. Furthermore, in a chicken embryo-based assay, CPC scaffolds demonstrated significantly higher angiogenic potential, indicating their ability to promote vascularisation, a crucial aspect for successful urethral reconstruction. Overall, these results suggest that CPC hybrid scaffolds containing both natural and synthetic components offer significant advantages over conventional acellular or synthetic materials alone. CPC scaffolds show promise as potential candidates for further research into the reconstruction of the urethrain vivo.

Plate Objective Scoring Tool (POST) in distal hypospadias: Correlation with post-repair complications.

OBJECTIVES: The Plate Objective Scoring Tool (POST) accurately reflects configuration of the urethral plate in distal hypospadias. Here we assessed whether POST score also correlates with patient risk of complications after surgical repair. METHODS: Data were obtained prospectively from pre-pubertal boys who underwent primary hypospadias repair between January 2020 and February 2023. Both POST and Glans-Urethral Meatus-Shaft (GMS) scores were determined in triplicate by three independent reviewers before evaluating correlation with complications after surgery. RESULTS: POST ratios were strongly correlated with incidence of post-repair complications in n = 121 patients. Mean POST score was 1.10 (range 0.5-1.62) and average GMS value was 5.29 ± 1.36 (median G = 2, M = 2, S = 1). Bivariate correlation analysis indicated that POST score can accurately predict risk of complications after surgery (Pearson correlation coefficient r = 0.821 [0.724-0.918], 95 % CI). A POST threshold of 1.2 provided the highest specificity for risk of post-operative complications, which occurred in 4.4 % of patients with POST score ≥1.2 (2/45 cases), compared with 25 % among patients with POST score <1.2 (19/76 cases). CONCLUSIONS: This study confirms that POST index can be used as a surrogate marker of urethral plate quality and accurately predicts the outcome of distal hypospadias repair. Objective scoring of POST revealed that low ratios were significantly associated with high risk of postoperative complications. In future, this approach could be used to stratify patients and better identify cases that require close follow-up care.

Quantification of vesicoureteral reflux using machine learning.

INTRODUCTION: The radiographic grading of voiding cystourethrogram (VCUG) images is often used to determine the clinical course and appropriate treatment in patients with vesicoureteral reflux (VUR). However, image-based evaluation of VUR remains highly subjective, so we developed a supervised machine learning model to automatically and objectively grade VCUG data. STUDY DESIGN: A total of 113 VCUG images were gathered from public sources to compile the dataset for this study. For each image, VUR severity was graded by four pediatric radiologists and three pediatric urologists (low severity scored 1-3; high severity 4-5). Ground truth for each image was assigned based on the grade diagnosed by a majority of the expert assessors. Nine features were extracted from each VCUG image, then six machine learning models were trained, validated, and tested using 'leave-one-out' cross-validation. All features were compared and contrasted, with the highest-ranked then being used to train the final models. RESULTS: F1-score is a metric that is often used to indicate performance accuracy of machine learning models. When using the highest-ranked VCUG image features, F1-scores for the support vector machine (SVM) and multi-layer perceptron (MLP) classifiers were 90.27 % and 91.14 %, respectively, indicating a high level of accuracy. When using all features combined, F1 scores were 89.37 % for SVM and 90.27 % for MLP. DISCUSSION: These findings indicate that a distorted pattern of renal calyces is an accurate predictor of high-grade VUR. Machine learning protocols can be enhanced in future to improve objective grading of VUR.

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.

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata.

Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.

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.

Management of neuropathic bladder secondary to spina bifida: Twenty years' experience with a conservative approach.

Introduction: Treatment of neuropathic bladder secondary to spina bifida is an ongoing challenge. Although different management strategies and protocols are available in the literature, reliance on expert opinion remains fundamental. A conservative approach can be utilized, but patients must be closely monitored throughout the management process. The objective of this study was to review the management and outcomes of neuropathic bladder in spina bifida by appraising long-term bladder and renal function in patients treated at a medical center utilizing a conservative management style. Methods: This is a single-center retrospective review of urology care for all spina bifida patients 5-19 years of age with a neuropathic bladder who attended follow-ups between April 2000 and April 2020. Only patients with more than 5 years of follow-up were included. Renal functions, continence and results of invasive video urodynamics (IUD) and any surgical interventions were recorded. Results: Seventy-one patients (mean age = 10.5 years) were identified after exclusions. Bladder compliance between first and last IUDs increased significantly (p = 0.0056). Anticholinergic treatment was started at the first outpatient appointment. Intravesical botulinum toxin injection was the second line treatment in ten patients. 94% of patients had an end fill pressure below 40 cm H2O in their last IUD. 82% were socially continent (dry or occasional damp patches) with or without catheterisations at the age of 11.5 years. One patient in the cohort had bladder augmentation. Conclusion: The optimal management of neuropathic bladder secondary to spina bifida remains controversial. Bladder and renal functional outcomes can be improved with close monitoring and less invasive management.

Ultrasonographic Evaluation of the Hypospadiac Penis in Children.

Introduction: Identifying key anatomical features of the hypospadiac penis is crucial to better understanding this pathology and guiding surgical reconstruction plans, thereby achieving superior functional and cosmetic outcomes. Objective: To Assess the feasibility and precision of penile ultrasonography (PUG) in determining key structural features for hypospadias cases (including distal extent of the spongiosal component of the urethral plate, to elucidate the healing process following tubularised incised-plate urethroplasty). Patients and Methods: Twenty-five children with hypospadias were assessed using PUG prior to surgical repair and then again under general anesthesia. Preoperative images were acquired using ultrasonography in sagittal and transverse planes, then later compared with anatomical findings obtained during surgical repair of urethral hypoplasia. Results: Median patient age was 1.2 years (range 0.5-12) and hypospadias types included coronal 17/25 (68%), mid-penile 5/25 (20%), and proximal penile 3/25 (12%). Distinct layers of the corpus spongiosa and mucosal layer, Buck fascia, tunica albuginea, glans, corpora cavernosa, and penile skin were delineated so that their spatial inter-relationship could be assessed. Distal extent of the spongiosal component of the urethral plate was determined by the mid-glans B-B line. The extent of urethral hypoplasia identified by PUG was relatively similar to measurements obtained intraoperatively. Conclusion: PUG is a feasible and accurate approach to evaluating penile configuration in children with hypospadias. Distal extent of the spongiosal component of the urethral plate was accurately determined, hence PUG could potentially be used to improve surgical planning and appraisal of current repair procedures.

An objective hypospadias classification system.

OBJECTIVE: Current approaches to classifying hypospadias severity are based on meatus position. These classification tools lack precise reflection on the degree of urethral hypoplasia and extent of urethral underdevelopment hence are not good representative of hypospadias severity. Here, an alternative classification system is introduced that objectively reflects the developmental pathology underlying this condition. The study goal was to appraise location of bifurcation in the corpus spongiosum (BCS) relative to the penile shaft as an indicator of hypospadias severity. PATIENTS AND METHODS: Patients aged <18 years with primary hypospadias were included in the study. Urethral defect ratio (UDR) was calculated by dividing the extent of urethral defect (distance between the glandular knobs and BCS) relative to stretched penile length (SPL). Hypospadias severity was then categorized into three distinct grades (UDR <0.5, 0.5-0.99, ≥1.0). The Inter-Class Correlation (ICC) was evaluated to assess the intra- and inter-rater agreement between the reviewers of UDC ratio. Linear regression analysis was performed to estimate the correlation between UDC ratios and either plate objective scoring tool (POST) and Curvature degrees. RESULTS: A total of 67 patients aged 12.3 ± 3.7 months with primary hypospadias were enrolled. UDR ranged between 0.2 and 1.3. A significant difference in hypospadias level was observed between UDR grades, which further correlated degree of curvature (P < 0.0001), urethral plate quality (P < 0.0001), and associated anomalies (P < 0.05). The Inter-Class Correlation (ICC) value to examine the intra- and inter-rater agreement between the two reviewers in UDC ratio was 0.998 (95% CI 0.998, 0.999). Regression analysis revealed that UDR and both POST and Curvature degree were significantly associated (P < 0.001). CONCLUSIONS: A hypospadias severity scoring system based on embryological etiology and urethral hypoplasia and assessed relative to the penile shaft represents an objective, feasible, and consistent tool. These results clearly indicate that the reviewers have had excellent consistency/reliability across their consecutive readings. This new system can facilitate objective description of hypospadias-spectrum anomalies and thus support precise communication between individual surgeons and centers.

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.

Plate Objective Scoring Tool: A new preoperative indicator of penile curvature degree in children with distal hypospadias.

OBJECTIVES: There is an unmet need for preoperative methods that surgeons can use to objectively quantify hypospadias anatomic variables and determine risk of penile curvature. We, therefore, assessed whether Plate Objective Scoring Tool measurements were correlated with degree of ventral curvature in affected children. METHODS: Patients undergoing distal hypospadias repair were enrolled into the study between January 2018 and December 2020 and were categorized independently by at least two surgeons using Plate Objective Scoring Tool. Scores were compared statistically to determine the degree of ventral curvature and requirement for correction. RESULTS: Sixty-five patients with a median age of 18 months (interquartile range 13-26) were enrolled into the study prior to surgery for primary distal hypospadias. Patient probability of significant postoperative curvature (>20°) was determined with moderate confidence using a cutoff Plate Objective Scoring Tool score of 1 (sensitivity 75%, specificity 60%). Presurgery Plate Objective Scoring Tool scores were negatively correlated with subsequent degree of curvature (r = -0.37, P = 0.003), with values <1.0 predicting >20° curvature. CONCLUSIONS: Plate Objective Scoring Tool scoring offers a succinct method of describing hypospadias severity and correlates well with postoperative outcomes. The Plate Objective Scoring Tool system can therefore be used to objectively predict the likelihood of penile curvature and aid communication between surgeons and researchers, as well as improving parental counseling.

QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model.

The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.

Evaluation of penile curvature in patients with hypospadias; gaps in the current practice and future perspectives.

BACKGROUND/PURPOSE: Penile curvature (PC) is a significant phenotypic anomaly associated with hypospadias that can affect hypospadias repair post-operative outcomes and impact on long-term quality of life as well as psychosexual wellbeing of affected patients. While several previous studies have attempted to define PC assessment criteria, there is still no accurate, reproducible, and reliable tool for quantifying severity. Our goal was to review the pros and cons of the current tools utilized for assessing the degree of PC in children, stressing on both strengths and limitations of each method. METHODS: A wide and deliberate review of the literature discussing the assessment of PC in hypospadias patients was conducted. We also draw on relevant methods employed in adults with PC and Peyronie's disease where a greater breadth of studies has been conducted. RESULTS: The appraisal outcomes combined with our recommendations were presented in a structured approach discussing the pre-, intra-, and post-operative evaluation of PC in patients with hypospadias. Critical appraisal of the evaluation tools in terms of availability, cost, objectivity, and potential reproducibility was presented. CONCLUSION: This review reflects on current tools used for assessing the degree of PC in children, highlighting both strengths and limitations of each method. A wide variety of approaches are currently being practiced or investigated, with each method displaying particular utility and reliability characteristics. Several approaches are currently being explored with high potential to overcome the current difficulties encountered when measuring PC both in clinical practice and research studies.