Forensic examination of living persons in 3D models.

Physical injuries caused by interpersonal violence or accidents are usually documented with photographs. In addition to standard injury photography using 2D photographs, the Institute *INSTITUT NAME BLINDED FOR REVIEW* uses a Botspot Botscan ® multi-camera device (Photobox; Aniwaa Ltd, Berlin, Germany) that allows for 3D documentation of a subject. The Photobox contains 70 cameras positioned at different heights looking at a central platform. Within a fraction of a second, all cameras are activated and acquire the necessary images for 3D documentation. In previous studies by Michienzi et al. (2018), the geometric correctness of 3D documented injuries was analyzed. While their work concentrated solely on artificial injuries and their dimensions, the work presented in this study analyzes whether the Photobox allows for accurate medical interpretation of injuries, by forensic pathologists. To perform this analysis, 40 datasets of a variety of real cases were processed to 3D models. The created 3D models were then examined by forensic pathologists on 2D computer screens, and the findings were compared with the original reports. While the aim of this work was to assess whether examinations based on a 3D model allows comparable results to immediate examinations of the subject, the results showed that examinations based on a 3D model are 85% accurate when comparing with physical examinations. This indicates that 3D models allow for reasonably accurate interpretation, and it is possible that accuracy might increase with improved equipment and better trained personnel.

Ischemic stroke in COVID-19 patients: Mechanisms, treatment, and outcomes in a consecutive Swiss Stroke Registry analysis.

BACKGROUND: Most case series of patients with ischemic stroke (IS) and COVID-19 are limited to selected centers or lack 3-month outcomes. The aim of this study was to describe the frequency, clinical and radiological features, and 3-month outcomes of patients with IS and COVID-19 in a nationwide stroke registry. METHODS: From the Swiss Stroke Registry (SSR), we included all consecutive IS patients ≥18 years admitted to Swiss Stroke Centers or Stroke Units during the first wave of COVID-19 (25 February to 8 June 2020). We compared baseline features, etiology, and 3-month outcome of SARS-CoV-2 polymerase chain reaction-positive (PCR+) IS patients to SARS-CoV-2 PCR- and/or asymptomatic non-tested IS patients. RESULTS: Of the 2341 IS patients registered in the SSR during the study period, 36 (1.5%) had confirmed COVID-19 infection, of which 33 were within 1 month before or after stroke onset. In multivariate analysis, COVID+ patients had more lesions in multiple vascular territories (OR 2.35, 95% CI 1.08-5.14, p = 0.032) and fewer cryptogenic strokes (OR 0.37, 95% CI 0.14-0.99, p = 0.049). COVID-19 was judged the likely principal cause of stroke in 8 patients (24%), a contributing/triggering factor in 12 (36%), and likely not contributing to stroke in 13 patients (40%). There was a strong trend towards worse functional outcome in COVID+ patients after propensity score (PS) adjustment for age, stroke severity, and revascularization treatments (PS-adjusted common OR for shift towards higher modified Rankin Scale (mRS) = 1.85, 95% CI 0.96-3.58, p = 0.07). CONCLUSIONS: In this nationwide analysis of consecutive ischemic strokes, concomitant COVID-19 was relatively rare. COVID+ patients more often had multi-territory stroke and less often cryptogenic stroke, and their 3-month functional outcome tended to be worse.

AI support for ethical decision-making around resuscitation: proceed with care.

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts.

Comparison of superficial wound documentation using 2D forensic photography, 3D photogrammetry, Botscan© and VR with real-life examination.

Evidence acquisition, interpretation and preservation are essential parts of forensic case work that make a standardized documentation process fundamental. The most commonly used method for the documentation and interpretation of superficial wounds is a combination of two modalities: two-dimensional (2D) photography for evidence preservation and real-life examination for wound analysis. As technologies continue to develop, 2D photography is being enhanced with three-dimensional (3D) documentation technology. In our study, we compared the real-life examination of superficial wounds using four different technical documentation and visualization methods.To test the different methods, a mannequin was equipped with several injury stickers, and then the different methods were applied. A total of 42 artificial injury stickers were documented in regard to orientation, form, color, size, wound borders, wound corners and suspected mechanism of injury for the injury mechanism. As the gold standard, superficial wounds were visually examined by two board-certified forensic pathologists directly on the mannequin. These results were compared to an examination using standard 2D forensic photography; 2D photography using the multicamera system Botscan©, which included predefined viewing positions all around the body; and 3D photogrammetric reconstruction based on images visualized both on screen and in a virtual reality (VR) using a head-mounted display (HMD).The results of the gold standard examination showed that the two forensic pathologists had an inter-reader agreement ranging from 69% for the orientation and 11% for the size of the wounds. A substantial portion of the direct visual documentation showed only a partial overlap, especially for the items of size and color, thereby prohibiting the statistical comparison of these two items. A forest plot analysis of the remaining six items showed no significant difference between the methods. We found that among the forensic pathologists, there was high variability regarding the vocabulary used for the description of wound morphology, which complicated the exact comparison of the two documentations of the same wound.There were no significant differences for any of the four methods compared to the gold standard, thereby challenging the role of real-life examination and 2D photography as the most reliable documentation approaches. Further studies with real injuries are necessary to support our evaluation that technical examination methods involving multicamera systems and 3D visualization for whole-body examination might be a valid alternative in future forensic documentation.