Battineni Gopi, Chintalapudi Nalini, Hossain Mohammad Amran, Losco Giuseppe, Ruocco Ciro, Sagaro Getu Gamo, Traini Enea, Nittari Giulio, Amenta Francesco
Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy.
School of Architecture and Design, University of Camerino, 63100 Ascoli Piceno, Italy.
Bioengineering (Basel). 2022 Aug 5;9(8):370. doi: 10.3390/bioengineering9080370.
The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.
主要在西方工业化世界，人口老龄化进程的加剧伴随着包括神经退行性疾病和成年期痴呆症在内的几种非传染性疾病发病率的上升。为了推动包括治疗和预防措施在内的适当干预措施，早期准确诊断是必要的。传统的磁共振成像（MRI）是诊断神经系统疾病的一种相当常见的技术。越来越多的证据表明，人工智能（AI）方法与MRI相结合对于提高不同类型痴呆症的诊断准确性特别有用。在这项工作中，我们系统地回顾了人工智能算法在成年期痴呆症早期检测中的特点，并讨论了其性能指标。我们使用三个数据库进行文献检索，即PubMed（Medline）、科学网和Scopus。检索仅限于2006年以后发表的英文文章。文章筛选采用基于纽卡斯尔-渥太华量表（NOS）评分的质量标准。只有NOS评分≥7的论文才被考虑进一步审查。文献检索共得到1876篇文章，由于重复，1195篇文章未被考虑。进行了多次筛选以评估质量标准，最终得到29项研究。所有选定的文章根据不同属性进一步分组，包括研究类型、用于识别痴呆症的人工智能模型类型、性能指标和数据类型。最常见的成年期痴呆症是阿尔茨海默病和血管性痴呆。与MRI相关的人工智能技术使诊断准确率提高到73.3%至99%。这些发现表明，人工智能应与传统的MRI技术相结合，以获得对老年痴呆症的精确早期诊断。
