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CC BY 4.0 · Arq Neuropsiquiatr 2024; 82(08): s00441788657
DOI: 10.1055/s-0044-1788657
Original Article

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis

Revolucionando a doença de Alzheimer precoce e o diagnóstico de comprometimento cognitivo leve: uma metanálise de aprendizagem profunda por ressonância magnética
Li-xue Wang 
1   Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China.
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
,
Yi-zhe Wang 
1   Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China.
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
,
Chen-guang Han 
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
3   Beijing Tsinghua Changgung Hospital, Department of Information Administration, Beijing, China.
,
Lei Zhao 
1   Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China.
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
,
Li He 
1   Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China.
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
,
Jie Li 
1   Beijing Tsinghua Changgung Hospital, Department of Radiology, Beijing, China.
2   Tsinghua University, School of Clinical Medicine, Beijing, China.
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Abstract

Background The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights.

Objective A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models.

Methods A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI.

Results A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone.

Conclusion Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Resumo

Antecedentes O diagnóstico precoce da doença de Alzheimer (DA) e do comprometimento cognitivo leve (CCL) continua sendo um desafio significativo na neurologia, com métodos convencionais frequentemente limitados pela subjetividade e variabilidade na interpretação. A integração da aprendizagem profunda com a inteligência artificial (IA) na análise de imagens de ressonância magnética surge como uma abordagem transformadora, oferecendo o potencial para insights diagnósticos imparciais e altamente precisos.

Objetivo Uma metanálise foi projetada para analisar a precisão diagnóstica do aprendizado profundo de imagens de ressonância magnética em modelos de DA e CCL.

Métodos Uma metanálise foi realizada nos bancos de dados das bibliotecas PubMed, Embase e Cochrane seguindo as diretrizes Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), com foco na precisão diagnóstica do aprendizado profundo. Posteriormente, a qualidade metodológica foi avaliada por meio do checklist QUADAS-2. Medidas diagnósticas, incluindo sensibilidade, especificidade, razões de verossimilhança, razão de chances diagnósticas e área sob a curva característica de operação do receptor (area under the receiver operating characteristic curve [AUROC]) foram analisadas, juntamente com análises de subgrupo para ressonância magnética ponderada em T1 e não ponderada em T1.

Resultados Um total de 18 estudos elegíveis foram identificados. O coeficiente de correlação de Spearman foi de -0,6506. A metanálise mostrou que a sensibilidade e a especificidade combinadas, a razão de verossimilhança positiva, a razão de verossimilhança negativa e a razão de chances de diagnóstico foram 0,84, 0,86, 6,0, 0,19 e 32, respectivamente. A AUROC foi de 0,92. O ponto quiescente do resumo hierárquico da característica de operação do receptor (hierarchical summary of receiver operating characteristic [HSROC]) foi 3,463. Notavelmente, as imagens de 12 estudos foram adquiridas apenas por ressonância magnética ponderada em T1, e as dos outros 6 foram obtidas apenas por ressonância magnética não ponderada em T1.

Conclusão Em geral, a aprendizagem profunda da ressonância magnética para o diagnóstico de DA e CCL mostrou boa sensibilidade e especificidade e contribuiu para melhorar a precisão diagnóstica.

Keywords

Alzheimer Disease - Cognitive Dysfunction - Magnetic Resonance Imaging - Deep Learning - Meta-Analysis

Palavras-chave

Doença de Alzheimer - Disfunção Cognitiva - Imageamento por Ressonância Magnética - Aprendizado Profundo - Metanálise

Authors' Contributions

LXW, JL: conceived and designed the study, conducted the study, edited the manuscript draft; YZW, CGH: contributed to data acquisition; CGH, LZ: analyzed the data; YZH, LZ: interpreted the data; XW, JL: reviewed and edited the manuscript. All authors have read and approved the manuscript.




Publikationsverlauf

Eingereicht: 22. Februar 2024

Angenommen: 06. Mai 2024

Artikel online veröffentlicht:
15. August 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

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Bibliographical Record
Li-xue Wang, Yi-zhe Wang, Chen-guang Han, Lei Zhao, Li He, Jie Li. Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis. Arq Neuropsiquiatr 2024; 82: s00441788657.
DOI: 10.1055/s-0044-1788657