Improving Medical Photography in a Level 1 Trauma Center by Implementing a Specialized Smartphone-Based App in Comparison to the Usage of Digital Cameras: Prospective Panel Study.

BACKGROUND: Medical photography plays a pivotal role in modern health care, serving multiple purposes ranging from patient care to medical documentation and education. Specifically, it aids in wound management, surgical planning, and medical training. While digital cameras have traditionally been used, smartphones equipped with specialized apps present an intriguing alternative. Smartphones offer several advantages, including increased usability and efficiency and the capability to uphold medicolegal standards more effectively and consistently. OBJECTIVE: This study aims to assess whether implementing a specialized smartphone app could lead to more frequent and efficient use of medical photography. METHODS: We carried out this study as a comprehensive single-center panel investigation at a level 1 trauma center, encompassing various settings including the emergency department, operating theaters, and surgical wards, over a 6-month period from June to November 2020. Using weekly questionnaires, health care providers were asked about their experiences and preferences with using both digital cameras and smartphones equipped with a specialized medical photography app. Parameters such as the frequency of use, time taken for image upload, and general usability were assessed. RESULTS: A total of 65 questionnaires were assessed for digital camera use and 68 for smartphone use. Usage increased significantly by 5.4 (SD 1.9) times per week (95% CI 1.7-9.2; P=.005) when the smartphone was used. The time it took to upload pictures to the clinical picture and archiving system was significantly shorter for the app (mean 1.8, SD 1.2 min) than for the camera (mean 14.9, SD 24.0 h; P<.001). Smartphone usage also outperformed the digital camera in terms of technical failure (4.4% vs 9.7%; P=.04) and for the technical process of archiving (P<.001) pictures to the picture archiving and communication system (PACS) and display images (P<.001) from it. No difference was found in regard to the photographer's intent (P=.31) or reasoning (P=.94) behind the pictures. Additionally, the study highlighted that potential concerns regarding data security and patient confidentiality were also better addressed through the smartphone app, given its encryption capabilities and password protection. CONCLUSIONS: Specialized smartphone apps provide a secure, rapid, and user-friendly platform for medical photography, showing significant advantages over traditional digital cameras. This study supports the notion that these apps not only have the potential to improve patient care, particularly in the realm of wound management, but also offer substantial medicolegal and economic benefits. Future research should focus on additional aspects such as patient comfort and preference, image resolution, and the quality of photographs, as well as seek to corroborate these findings through a larger sample size.

[Interdisciplinary networking for optimal treatment decision-making : Fast decision-making in acute ischemic stroke management]. / Mittels interdisziplinärer Vernetzung zur optimalen Therapieentscheidung : Der schnelle Weg zur Therapieentscheidung beim akuten Schlaganfall.

Every year, about 270,000 strokes occur in Germany. In the entire DACH region (Germany, Austria, Switzerland), more than 310,000 cases are reported each year. Two thirds of the surviving patients are dependent on external assistance after the stroke. Increasingly, imaging data are becoming the focus of treatment decisions. These data provide critical information about the location and extent of vessel occlusion, infarct size, volume of salvageable brain tissue, and degree of collateralization. Certified stroke units and stroke networks already specialize in state-of-the-art therapeutic options, but they need additional information technology tools to deliver the right therapy to the right patient population as quickly as possible. For multidisciplinary, seamless support in stroke care, both prehospital and in-hospital processes need to be optimized. This article presents a concept for supraregional stroke care by means of networking all involved actors in the prehospital as well as in the in-hospital area. Further needs analyses should ensure the implementation as well as the generalizability to different regions.

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study.

In clinical routine, wound documentation is one of the most important contributing factors to treating patients with acute or chronic wounds. The wound documentation process is currently very time-consuming, often examiner-dependent, and therefore imprecise. This study aimed to validate a software-based method for automated segmentation and measurement of wounds on photographic images using the Mask R-CNN (Region-based Convolutional Neural Network). During the validation, five medical experts manually segmented an independent dataset with 35 wound photographs at two different points in time with an interval of 1 month. Simultaneously, the dataset was automatically segmented using the Mask R-CNN. Afterwards, the segmentation results were compared, and intra- and inter-rater analyses performed. In the statistical evaluation, an analysis of variance (ANOVA) was carried out and dice coefficients were calculated. The ANOVA showed no statistically significant differences throughout all raters and the network in the first segmentation round (F = 1.424 and p > 0.228) and the second segmentation round (F = 0.9969 and p > 0.411). The repeated measure analysis demonstrated no statistically significant differences in the segmentation quality of the medical experts over time (F = 6.05 and p > 0.09). However, a certain intra-rater variability was apparent, whereas the Mask R-CNN consistently provided identical segmentations regardless of the point in time. Using the software-based method for segmentation and measurement of wounds on photographs can accelerate the documentation process and improve the consistency of measured values while maintaining quality and precision.

Evaluating Usability of a Touchless Image Viewer in the Operating Room.

BACKGROUND: Availability of patient-specific image data, gathered from preoperatively conducted studies, like computed tomography scans and magnetic resonance imaging studies, during a surgical procedure is a key factor for surgical success and patient safety. Several alternative input methods, including recognition of hand gestures, have been proposed for surgeons to interact with medical image viewers during an operation. Previous studies pointed out the need for usability evaluation of these systems. OBJECTIVES: We describe the accuracy and usability of a novel software system, which integrates gesture recognition via machine learning into an established image viewer. METHODS: This pilot study is a prospective, observational trial, which asked surgeons to interact with software to perform two standardized tasks in a sterile environment, modeled closely to a real-life situation in an operating room. To assess usability, the validated "System Usability Scale" (SUS) was used. On a technical level, we also evaluated the accuracy of the underlying neural network. RESULTS: The neural network reached 98.94% accuracy while predicting the gestures during validation. Eight surgeons with an average of 6.5 years of experience participated in the usability study. The system was rated on average with 80.25 points on the SUS. CONCLUSION: The system showed good overall usability; however, additional areas of potential improvement were identified and further usability studies are needed. Because the system uses standard PC hardware, it made for easy integration into the operating room.