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All Journal e-CliniC JURNAL BIOMEDIK Jurnal Kedokteran Klinik Jurnal Sinaps Jurnal Kedokteran Meditek
Rizal Tumewah
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Biochemistry, Genetics & Molecular Biology Education Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Public Health

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Prediksi Gangguan Kognitif Ringan Menggunakan Pencitraan Resonansi Magnetik dan Deep Learning: Sebuah Studi Meta-Analisis Setiawan, Budi; Wariki, Windy Mariane Virenia; Warouw, Finny; Momole, Ansye Grace Nancy; Tumewah, Rizal; Pertiwi, Junita Maja
Jurnal Kedokteran Meditek Vol 31 No 2 (2025): MARCH
Publisher : Fakultas Kedokteran Universitas Kristen Krida Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36452/jkdoktmeditek.v31i2.3463

Abstract

Introduction: Mild Cognitive Impairment (MCI) is a condition characterized by cognitive decline that does not interfere with daily activities but increases the risk of progressing to Alzheimer's Dementia (AD). Early detection of MCI progression to AD is crucial for early intervention. Purpose: The purpose of this meta-analysis to evaluate the performance of Convolutional Neural Networks (CNNs), an artificial intelligence used to analyze complex data such as images, in predicting the conversion of MCI to AD using MRI data. Methods: A meta-analysis was conducted following PRISMA guidelines, utilizing articles from PubMed and Wiley Online Library. Inclusion criteria focused on studies that used CNN in conjunction with MRI to diagnose MCI. A total of 39 articles with 40 comparative studies were analyzed. Results: The CNN showed an average accuracy of 0.7910 (p < 0.0001), sensitivity of 0.7698, specificity of 0.8467, and an Area Under the Curve (AUC) of 0.8063. High heterogeneity (I² = 90.90%) indicated significant variation across studies. Sub meta-analysis revealed consistent performance despite the heterogeneity. Conclusion: CNN is potentially useful for predicting the progression of MCI to AD. Further research is needed to address heterogeneity, improve algorithms, expand datasets, and include more diverse populations.
Co-Authors Abrar Arham Arthur H.P. Mawuntu, Arthur H.P. Bernadus, Janno Berty Bradly Budi Setiawan Caroline G. Senaen Corry N. Mahama David Susanto Febrilya F. Tarukbua, Febrilya F. Gilbert Tangkudung Glen Y. C. R. Kabi Hasibuan, Mughni H. Junita Maja Pertiwi Junita Maja PS, Junita Maja Maria Theresia Jasi Mawuntu, Arthur H P Mieke A. H. N Kembuan Mieke A. H. N. Kembuan Momole, Ansye Grace Nancy Pertiwi, Junita Maja Rachel Mongisidi Ricky Gunawan Sumampouw, Ezra L.P Sutrisno, Pedro Wariki, Windy M. V. Warouw, Finny