Incidence, management and outcomes in hepatic artery complications after paediatric liver transplantation: protocol of the retrospective, international, multicentre HEPATIC Registry.

INTRODUCTION: Hepatic artery complications (HACs), such as a thrombosis or stenosis, are serious causes of morbidity and mortality after paediatric liver transplantation (LT). This study will investigate the incidence, current management practices and outcomes in paediatric patients with HAC after LT, including early and late complications. METHODS AND ANALYSIS: The HEPatic Artery stenosis and Thrombosis after liver transplantation In Children (HEPATIC) Registry is an international, retrospective, multicentre, observational study. Any paediatric patient diagnosed with HAC and treated for HAC (at age <18 years) after paediatric LT within a 20-year time period will be included. The primary outcomes are graft and patient survivals. The secondary outcomes are technical success of the intervention, primary and secondary patency after HAC intervention, intraprocedural and postprocedural complications, description of current management practices, and incidence of HAC. ETHICS AND DISSEMINATION: All participating sites will obtain local ethical approval and (waiver of) informed consent following the regulations on the conduct of observational clinical studies. The results will be disseminated through scientific presentations at conferences and through publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: The HEPATIC registry is registered at the ClinicalTrials.gov website; Registry Identifier: NCT05818644.

Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review.

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new opportunities to advance cardiothoracic surgical performance with innovations from computer vision and artificial intelligence. These technological innovations are critically evaluated in terms of the benefits they could offer the cardiothoracic surgical community, and any barriers to the uptake of the technology are elaborated upon. Like some other specialities, cardiothoracic surgery has relatively few opportunities to benefit from tools with data capture technology embedded within them (as is possible with robotic-assisted laparoscopic surgery, for example). In such cases, pose estimation techniques that allow for movement tracking across a conventional operating field without using specialist equipment or markers offer considerable potential. With video data from either simulated or real surgical procedures, these tools can (1) provide insight into the development of expertise and surgical performance over a surgeon's career, (2) provide feedback to trainee surgeons regarding areas for improvement, (3) provide the opportunity to investigate what aspects of skill may be linked to patient outcomes which can (4) inform the aspects of surgical skill which should be focused on within training or mentoring programmes. Classifier or assessment algorithms that use artificial intelligence to 'learn' what expertise is from expert surgical evaluators could further assist educators in determining if trainees meet competency thresholds. With collaborative efforts between surgical teams, medical institutions, computer scientists and researchers to ensure this technology is developed with usability and ethics in mind, the developed feedback tools could improve cardiothoracic surgical practice in a data-driven way.

Pose-based tremor type and level analysis for Parkinson's disease from video.

PURPOSE: Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. METHODS: We propose to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. RESULTS: We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. CONCLUSION: Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.