Impact of Camera Deviation on Penile Curvature Assessment Using 2D Pictures.

BACKGROUND: An accurate curvature assessment (CA) is required in the decision-making process for patients with Peyronie's disease. In-office CA following induced erection is the gold standard for CA, although penile photography is commonly used due to its convenience. Camera deviations during 2D image acquisition might affect CA accuracy. AIM: To investigate the impact of camera angle deviations on CA. METHODS: 2D pictures were taken from 5 models with a known uniplanar curvature (40°, 45°, 60°, 90°, and 120°). The model was kept on a fixed point and the camera was rotated around it. Pictures were taken with every 10° increase in camera deviation from the optimal position. The camera rotated to a maximum of 90° deviation in both the vertical and horizontal planes. The pictures were analyzed by 2 different urologists using a goniometer. The expected apparent curvature (AC) and the corresponding picture assessment error (PAE = AC - real model curvature) were also calculated for each picture using trigonometry principles. MAIN OUTCOME MEASURE: Assessing PAE magnitude and patterns was our primary outcome. Secondary outcomes were intraobserver, interobserver, and observer-AC intraclass correlation coefficient (ICC). RESULTS: 100 pictures were analyzed. Intraobserver reliability was high (ICC = 0.99) for both urologists. Interobserver and observer-AC correlation were also high (ICC = 0.996 and ICC = 0.992, respectively). When the camera rotated in the horizontal axis, the PAE underestimated the curvature for models with curvatures smaller than 90° and overestimated the reading of the 120° model. When the camera rotated in the vertical axis, PAE had an inverse effect. The PAE showed a tendency to increase exponentially with higher deviation, reaching almost 100% for a deviation of 80°. Nevertheless, analyzing its magnitude regardless of the curvature, PAE was always <5% for camera deviations of 0-20°. CLINICAL IMPLICATIONS: If using picture-based CA, clinicians should attempt to take a picture perpendicular to the curvature plane for the most accurate measurement in degrees. Many clinicians request that patients take 3 pictures in a standard fashion (craniocaudal, lateral, and frontal), and if this technique is to be used, an extra picture is recommended. STRENGTH & LIMITATIONS: In our controlled environment, we were able to isolate CA errors due to camera angles from other confounders such as erection hardness. As a consequence, however, our results cannot be easily generalized. CONCLUSION: PAE due to non-optimal camera position is a complex phenomenon that affects CA depending on the rotation axis and the degree of penile curvature. Nevertheless, PAE is always <5% for camera deviations of 0-20°. Nascimento B, Cerqueira I, Miranda EP, et al. Impact of Camera Deviation on Penile Curvature Assessment Using 2D Pictures. J Sex Med 2018;15:1638-1644.

The Consciousness Domain Index: External Validation and Prognostic Relevance of a Data-Driven Assessment.

The prognosis of neurological outcomes in patients with prolonged Disorders of Consciousness (pDoC) has improved in the last decades. Currently, the level of consciousness at admission to post-acute rehabilitation is diagnosed by the Coma Recovery Scale-Revised (CRS-R) and this assessment is also part of the used prognostic markers. The consciousness disorder diagnosis is based on scores of single CRS-R sub-scales, each of which can independently assign or not a specific level of consciousness to a patient in a univariate fashion. In this work, a multidomain indicator of consciousness based on CRS-R sub-scales, the Consciousness-Domain-Index (CDI), was derived by unsupervised learning techniques. The CDI was computed and internally validated on one dataset (N=190) and then externally validated on another dataset (N=86). Then, the CDI effectiveness as a short-term prognostic marker was assessed by supervised Elastic-Net logistic regression. The prediction accuracy of the neurological prognosis was compared with models trained on the level of consciousness at admission based on clinical state assessments. CDI-based prediction of emergence from a pDoC improved the clinical assessment-based one by 5.3% and 3.7%, respectively for the two datasets. This result confirms that the data-driven assessment of consciousness levels based on multidimensional scoring of the CRS-R sub-scales improve short-term neurological prognosis with respect to the classical univariately-derived level of consciousness at admission.