de Filippis Renato, Carbone Elvira Anna, Gaetano Raffaele, Bruni Antonella, Pugliese Valentina, Segura-Garcia Cristina, De Fazio Pasquale
Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
Neuropsychiatr Dis Treat. 2019 Jun 19;15:1605-1627. doi: 10.2147/NDT.S202418. eCollection 2019.
Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders.
A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls.
We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors.
Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network).
The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.
精神分裂症（SCZ）的诊断完全基于临床，因为能够准确预测该疾病的特定生物标志物仍然未知。机器学习（ML）是一种很有前景的方法，可以辅助临床医生诊断精神障碍。
根据PRISMA声明进行了一项系统综述，以评估其区分SCZ患者与健康对照的准确性。
我们在2018年12月之前系统检索了PubMed、Embase、MEDLINE、PsychINFO和Cochrane图书馆，使用ML技术和SCZ的通用术语，无语言或时间限制。本综述纳入了35项研究：其中8项使用结构神经影像学，26项使用功能神经影像学，1项两者都用，最低准确率>60%（大多数为75-90%）。从每个出版物中提取敏感性、特异性和准确率，或直接从作者处获得。
支持向量机是最常用的技术，如果与其他ML技术结合使用，准确率接近100%。前额叶和颞叶皮质似乎是诊断SCZ最有用的脑区。ML分析可以有效检测SCZ患者大脑连接性的显著改变（例如，默认模式网络、视觉网络、感觉运动网络、额顶网络和突显网络）。
这些预测模型以及多种ML技术整合产生的新模型所显示出的更高准确率，对于SCZ患者的早期诊断、治疗反应评估和预后判断将越来越具有决定性意义。为了给患者带来真正的益处，未来的挑战是不仅要通过临床评估，还要借助ML算法实现准确诊断。
