Mandometer Clinic.
Department of Neurobiology, Care Sciences and Society, Karolinska Institutet.
J Vis Exp. 2022 May 10(183). doi: 10.3791/63848.
Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of 13% and are associated with severe physical and psychosocial problems. Early diagnosis, which is important for effective treatment and prevention of undesirable long-term health consequences, imposes problems among non-specialist clinicians unfamiliar with these patients, such as those working in primary care. Early, accurate diagnosis, particularly in primary care, allows expert interventions early enough in the disorder to facilitate positive treatment outcomes. Computer-assisted diagnostic procedures offer a possible solution to this problem by providing expertise via an algorithm that has been developed from a large number of cases that have been diagnosed in person by expert diagnosticians and expert caregivers. A web-based system for determining an accurate diagnosis for patients suspected to suffer from an eating disorder was developed based on these data. The process is automated using an algorithm that estimates the respondent's probability of having an eating disorder and the type of eating disorder the individual has. The system provides a report that works as an aid for clinicians during the diagnostic process and serves as an educational tool for new clinicians.
进食障碍（神经性厌食症、神经性贪食症、暴食障碍和其他特定的进食或喂养障碍）的总体患病率为 13%，与严重的身体和心理社会问题相关。早期诊断对于有效的治疗和预防不良的长期健康后果很重要，但对于不熟悉这些患者的非专家临床医生（如初级保健医生）来说，这是一个问题。早期、准确的诊断，特别是在初级保健中，使专家干预能够在疾病的早期进行，从而促进积极的治疗结果。计算机辅助诊断程序通过提供算法的专业知识来解决这个问题，该算法是从大量由专家诊断人员和专家护理人员亲自诊断的病例中开发出来的。基于这些数据，开发了一种用于确定疑似患有进食障碍的患者的准确诊断的基于网络的系统。该过程使用一种算法实现自动化，该算法估计被调查者患有进食障碍的概率以及个人患有哪种进食障碍。该系统提供一份报告，作为临床医生在诊断过程中的辅助工具，并作为新临床医生的教育工具。
