Bristol Institute of Clinical Neurosciences, University of Bristol, Southmead Hospital, Bristol, UK.
Faculty of Health Sciences, University of Bristol, Bristol, UK.
Adv Med Sci. 2019 Sep;64(2):292-302. doi: 10.1016/j.advms.2019.03.002. Epub 2019 Apr 2.
Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm.
PATIENTS & METHODS: An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52-80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of ‒1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease.
On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole.
The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.
额颞叶痴呆（FTD）是一种与预后不良和生活质量大幅下降相关的神经退行性疾病。FTD 的误诊率非常高，患者通常要等待数年才能得到明确诊断。本研究使用一种新的基于人工智能的算法来调查 FTD 的误诊现状。
开发了一种人工智能算法，以回顾性分析 47 名被诊断为 FTD（年龄 52-80 岁）的患者的就诊经历。该算法通过利用-1 到+1 的奖励信号来评估疾病的行为和语言变体中的症状、影像学技术和临床判断，从而分析患者路径的效率。
平均而言，每位患者接受了 4.93 次检查，其中 67.4%为影像学扫描。从首次就诊到明确诊断平均需要 939 天。平均预约间隔为 204 天，平均每位患者在就诊过程中被改变诊断 7.37 次。该算法通过评估导致个体和整体奖励信号减少的干预措施，提出了改进建议。
该研究证明该算法能够有效地指导临床实践，提高 FTD 的诊断准确性，同时使审核过程更快、更经济可行。
