Aim The aim of this study was to investigate the detailed, in situ, morphology of Denonvilliers’ fascia (DVF) in cadavers using sheet plastination and confocal microscopy and to review and describe the optimal anterior plane for mobilization of the distal rectum. Method Six male cadavers (age range 46–87 years) were prepared as six sets of transverse (× 2), coronal (× 1) and sagittal (× 3) plastinated sections which were examined under a confocal laser scanning microscope. Results In this study a consistent space between the anterior rectal wall and the posterior surface of the prostate and seminal vesicles above the level of the perineal body was termed the prerectal space. Within that prerectal space we identified fibres which take their origin from the external urethral sphincter (EUS), together with others from the longitudinal rectal muscle (LRM) and the connective tissue sheaths of neurovascular bundles. Neither the EUS‐ nor the LRM‐originated fibres were continuous with the endopelvic fascia; they were interposed laterally and cranially by multiple neurovascular bundles. Further, our results suggest that the peritoneum does not descend deep within the prerectal space. Conclusion This study reveals the undisturbed, in situ, structural detail of membrane‐like structures in the prerectal space and confirms that the optimal plane for anterolateral mobilization of the rectum is posterior to the multilayered Denonvilliers’ fascia.

<abstract><p>This systematic review aims to investigate recent developments in the area of arc fault detection. The rising demand for electricity and concomitant expansion of energy systems has resulted in a heightened risk of arc faults and the likelihood of related fires, presenting a matter of considerable concern. To address this challenge, this review focuses on the role of artificial intelligence (AI) in arc fault detection, with the objective of illuminating its advantages and identifying current limitations. Through a meticulous literature selection process, a total of 63 articles were included in the final analysis. The findings of this review suggest that AI plays a significant role in enhancing the accuracy and speed of detection and allowing for customization to specific types of faults in arc fault detection. Simultaneously, three major challenges were also identified, including missed and false detections, the restricted application of neural networks and the paucity of relevant data. In conclusion, AI has exhibited tremendous potential for transforming the field of arc fault detection and holds substantial promise for enhancing electrical safety.</p></abstract>

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Background and aim Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery. Methods This study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs). Results The intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 ( p < .001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 ( p < .001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm 2 to 76.31 ± 11.31mm 2 (all p < .001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients. Conclusion This technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients’ photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.