Revolucionando a doença de Alzheimer precoce e o diagnóstico de comprometimento cognitivo leve: uma metanálise de aprendizagem profunda por ressonância magnética

Wang, Li-xue; Wang, Yi-zhe; Han, Chen-guang; Zhao, Lei; He, Li; Li, Jie

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Sumário

Original article • Arq. Neuro-Psiquiatr. 82 (8) • 2024 • https://doi.org/10.1055/s-0044-1788657 copiar

Revolucionando a doença de Alzheimer precoce e o diagnóstico de comprometimento cognitivo leve: uma metanálise de aprendizagem profunda por ressonância magnética

Autoria SCIMAGO INSTITUTIONS RANKINGS

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.

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

Author	Year	Country	MRI	Sample size		Database	Type of research
Author	Year	Country	MRI	AD	MCI	Database	Type of research
Li H²⁶26 Li H. Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer's disease. Biomed Signal Process Control 2023;82:104571. Doi: 10.1016/j.bspc.2023.104571 https://doi.org/10.1016/j.bspc.2023.1045...	2023	China	structural Magnetic Resonance Imaging(sMRI)	151	142	ADNI	Retrospective
Agarwal D¹⁷17 Agarwal D, Berbís MA, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimés Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47(01):57. Doi: 10.1007/s10916-023-01941-4 https://doi.org/10.1007/s10916-023-01941...	2023	Spain	T1-weighted brain MRI	229	229	ADNI	Retrospective
Tanveer M³⁰30 Tanveer M, Rashid AH, Ganaie MA, Reza M, Razzak I, Hua KL. Classification of Alzheimer's Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning. IEEE J Biomed Health Inform 2022;26(04):1453-1463. Doi: 10.1109/JBHI.2021.3083274 https://doi.org/10.1109/JBHI.2021.308327...	2022	India	T1-weighted brain MRI	187	398	ADNI	Retrospective
Gao L²³23 Gao L,Hu Z, LiR, et al. Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases. Brain Sci 2022;12(10):1348. Doi: 10.3390/brainsci12101348 https://doi.org/10.3390/brainsci12101348...	2022	China	T1-weighted brain MRI	154	145	ANDI	Retrospective
Chen X²¹21 Chen X, Tang M, Liu A, Wei X. Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI. Ann Transl Med 2022;10(14):765. Doi: 10.21037/atm-22-2961 https://doi.org/10.21037/atm-22-2961...	2022	China	T2-weighted brain MRI	43	97	ADNI	Retrospective
Agarwal D¹⁸18 Agarwal D, Berbis MA, Martín-Noguerol T, Luna A, Garcia SCP, de la Torre-Díez I. End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer's Diagnosis. Mathematics 2022;10(15):2575. Doi: 10.3390/math10152575 https://doi.org/10.3390/math10152575...	2022	Spain	T1-weighted brain MRI	245	229	ADNI + IXI	Retrospective
Mehmood A²⁷27 Mehmood A, Yang S, Feng Z, et al. ATransfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience 2021;460:43-52. Doi: 10.1016/j.neuroscience.2021.01.002 https://doi.org/10.1016/j.neuroscience.2...	2021	China	T1-weighted brain MRI	85	70	ADNI	Retrospective
Kang W²⁵25 Kang W, Lin L, Zhang B, Shen X, Wu SAlzheimer's Disease Neuroimaging Initiative. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis. Comput Biol Med 2021; 136:104678. Doi: 10.1016/j.compbiomed.2021.104678 https://doi.org/10.1016/j.compbiomed.202...	2021	China	T1-weighted brain MRI	187	382	ADNI	Retrospective
Hedayati R²⁴24 Hedayati R, Khedmati M, Taghipour-Gorjikolaie M. Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer's disease diagnosis. Biomed Signal Process Control 2021;66(03):102397. Doi: 10.1016/j.bspc.2020.102397 https://doi.org/10.1016/j.bspc.2020.1023...	2021	Iran	Functional magnetic resonance imaging (fMRI)	100	100	ADNI	Retrospective
Akramifard H¹⁹19 Akramifard H, BalafarMA, Razavi SN, Ramli AR. Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders. J Med Signals Sens 2021;11(02):120-130. Doi: 10.4103/jmss.JMSS_11_20 https://doi.org/10.4103/jmss.JMSS_11_20...	2021	Iran	Not explicitly stated	156	338	ADNI	Retrospective
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	South Korea	T1-weighted brain MRI	161	363	Asan Medical Center	Retrospective
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	South Korea	T1-weighted brain MRI	68	63	Kyung Hee University Hospital at Gangdong	Retrospective
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	South Korea	T1-weighted brain MRI	178	317	ADNI	Retrospective
Feng W²²22 Feng W, Halm-Lutterodt NV, Tang H, et al. Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process. Int J Neural Syst 2020;30(06):2050032. Doi: 10.1142/S012906572050032X https://doi.org/10.1142/S012906572050032...	2020	China	T1-weighted brain MRI	130	133	ADNI	Retrospective
Wee C Y³¹31 Wee CY, Liu C, Lee A, Poh JS, Ji H, Qiu AAlzheimers Disease Neuroimage Initiative. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. Neuroimage Clin 2019;23:101929. Doi: 10.1016/j.nicl.2019.101929 https://doi.org/10.1016/j.nicl.2019.1019...	2019	Singapore	T1-weighted brain MRI	592	899	ADNI	Retrospective
Oh K²⁸28 Oh K, Chung YC, Kim KW, Kim WS, Oh IS. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep 2019;9(01): 18150. Doi: 10.1038/s41598-019-54548-6 https://doi.org/10.1038/s41598-019-54548...	2019	South Korea	T1-weighted brain MRI	198	101	ADNI	Retrospective
Basaia S²⁰20 Basaia S, Agosta F, Wagner L, et al;Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645. Doi: 10.1016/j.nicl.2018.101645 https://doi.org/10.1016/j.nicl.2018.1016...	2019	Italy	T1 and T2-weighted brain MRI	418	533	ADNI + Milan dataset	Retrospective
Basaia S²⁰20 Basaia S, Agosta F, Wagner L, et al;Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645. Doi: 10.1016/j.nicl.2018.101645 https://doi.org/10.1016/j.nicl.2018.1016...	2019	Italy	T1 and T2-weighted MRI	294	510	ADNI	Retrospective

Author	Year	Modeling	Test Set	TP	FP	FN	TN
Li H²⁶26 Li H. Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer's disease. Biomed Signal Process Control 2023;82:104571. Doi: 10.1016/j.bspc.2023.104571 https://doi.org/10.1016/j.bspc.2023.1045...	2023	CNN (EfficientNet)	AD:30,MCI: 30	27	2	3	28
Agarwal D¹⁷17 Agarwal D, Berbís MA, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimés Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47(01):57. Doi: 10.1007/s10916-023-01941-4 https://doi.org/10.1007/s10916-023-01941...	2023	3D CNN (EfficientNet-B0)	AD:29,MCI: 29	29	4	0	25
Tanveer M³⁰30 Tanveer M, Rashid AH, Ganaie MA, Reza M, Razzak I, Hua KL. Classification of Alzheimer's Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning. IEEE J Biomed Health Inform 2022;26(04):1453-1463. Doi: 10.1109/JBHI.2021.3083274 https://doi.org/10.1109/JBHI.2021.308327...	2022	CNN + Transfer Learning	20%	37	1	1	79
Gao L²³23 Gao L,Hu Z, LiR, et al. Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases. Brain Sci 2022;12(10):1348. Doi: 10.3390/brainsci12101348 https://doi.org/10.3390/brainsci12101348...	2022	LSTM networks + CNN	AD:31,MCI:18	25	2	6	16
Chen X²¹21 Chen X, Tang M, Liu A, Wei X. Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI. Ann Transl Med 2022;10(14):765. Doi: 10.21037/atm-22-2961 https://doi.org/10.21037/atm-22-2961...	2022	CNN + iterated RF	AD:30,MCI: 30	28	2	2	28
Agarwal D¹⁸18 Agarwal D, Berbis MA, Martín-Noguerol T, Luna A, Garcia SCP, de la Torre-Díez I. End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer's Diagnosis. Mathematics 2022;10(15):2575. Doi: 10.3390/math10152575 https://doi.org/10.3390/math10152575...	2022	3D CNN (DenseNet264)	AD:29,MCI: 29	27	10	2	19
Mehmood A²⁷27 Mehmood A, Yang S, Feng Z, et al. ATransfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience 2021;460:43-52. Doi: 10.1016/j.neuroscience.2021.01.002 https://doi.org/10.1016/j.neuroscience.2...	2021	CNN + Transfer Learning	20%	13	2	2	12
Kang W²⁵25 Kang W, Lin L, Zhang B, Shen X, Wu SAlzheimer's Disease Neuroimaging Initiative. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis. Comput Biol Med 2021; 136:104678. Doi: 10.1016/j.compbiomed.2021.104678 https://doi.org/10.1016/j.compbiomed.202...	2021	2D CNNs	20%	26	35	12	42
Hedayati R²⁴24 Hedayati R, Khedmati M, Taghipour-Gorjikolaie M. Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer's disease diagnosis. Biomed Signal Process Control 2021;66(03):102397. Doi: 10.1016/j.bspc.2020.102397 https://doi.org/10.1016/j.bspc.2020.1023...	2021	CNN	AD:20,MCI: 20	17	1	3	19
Akramifard H¹⁹19 Akramifard H, BalafarMA, Razavi SN, Ramli AR. Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders. J Med Signals Sens 2021;11(02):120-130. Doi: 10.4103/jmss.JMSS_11_20 https://doi.org/10.4103/jmss.JMSS_11_20...	2021	SVM + Auto-Encoder Neural Networks	10%	8	6	8	28
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	CNN + XGBoost	20%	23	19	10	54
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	CNN + XGBoost	20%	10	3	4	10
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	CNN + XGBoost	20%	24	19	12	45
Feng W²²22 Feng W, Halm-Lutterodt NV, Tang H, et al. Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process. Int J Neural Syst 2020;30(06):2050032. Doi: 10.1142/S012906572050032X https://doi.org/10.1142/S012906572050032...	2020	3D CNN + SVM	AD:23,MCI: 24	22	1	1	23
Wee C Y³¹31 Wee CY, Liu C, Lee A, Poh JS, Ji H, Qiu AAlzheimers Disease Neuroimage Initiative. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. Neuroimage Clin 2019;23:101929. Doi: 10.1016/j.nicl.2019.101929 https://doi.org/10.1016/j.nicl.2019.1019...	2019	CNN	10%	42	13	18	77
Oh K²⁸28 Oh K, Chung YC, Kim KW, Kim WS, Oh IS. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep 2019;9(01): 18150. Doi: 10.1038/s41598-019-54548-6 https://doi.org/10.1038/s41598-019-54548...	2019	3D CNN + CAE	10%	15	3	5	8
Basaia S²⁰20 Basaia S, Agosta F, Wagner L, et al;Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645. Doi: 10.1016/j.nicl.2018.101645 https://doi.org/10.1016/j.nicl.2018.1016...	2019	3D CNN	10%	35	6	7	48
Basaia S²⁰20 Basaia S, Agosta F, Wagner L, et al;Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645. Doi: 10.1016/j.nicl.2018.101645 https://doi.org/10.1016/j.nicl.2018.1016...	2019	3D CNN	10%	25	6	5	45

Author	Year	Patient selection	Index test	Reference standard	Flow and timing
Li H²⁶26 Li H. Attention-based and micro designed EfficientNetB2 for diagnosis of Alzheimer's disease. Biomed Signal Process Control 2023;82:104571. Doi: 10.1016/j.bspc.2023.104571 https://doi.org/10.1016/j.bspc.2023.1045...	2023	low	unclear	low	low
Agarwal D¹⁷17 Agarwal D, Berbís MA, Luna A, Lipari V, Ballester JB, de la Torre-Díez I. Automated Medical Diagnosis of Alzheimés Disease Using an Efficient Net Convolutional Neural Network. J Med Syst 2023; 47(01):57. Doi: 10.1007/s10916-023-01941-4 https://doi.org/10.1007/s10916-023-01941...	2023	unclear	unclear	low	low
Tanveer M³⁰30 Tanveer M, Rashid AH, Ganaie MA, Reza M, Razzak I, Hua KL. Classification of Alzheimer's Disease Using Ensemble of Deep Neural Networks Trained Through Transfer Learning. IEEE J Biomed Health Inform 2022;26(04):1453-1463. Doi: 10.1109/JBHI.2021.3083274 https://doi.org/10.1109/JBHI.2021.308327...	2022	unclear	low	low	low
Gao L²³23 Gao L,Hu Z, LiR, et al. Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases. Brain Sci 2022;12(10):1348. Doi: 10.3390/brainsci12101348 https://doi.org/10.3390/brainsci12101348...	2022	unclear	unclear	low	low
Chen X²¹21 Chen X, Tang M, Liu A, Wei X. Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI. Ann Transl Med 2022;10(14):765. Doi: 10.21037/atm-22-2961 https://doi.org/10.21037/atm-22-2961...	2022	unclear	unclear	low	low
Agarwal D¹⁸18 Agarwal D, Berbis MA, Martín-Noguerol T, Luna A, Garcia SCP, de la Torre-Díez I. End-to-End Deep Learning Architectures Using 3D Neuroimaging Biomarkers for Early Alzheimer's Diagnosis. Mathematics 2022;10(15):2575. Doi: 10.3390/math10152575 https://doi.org/10.3390/math10152575...	2022	unclear	unclear	low	low
Mehmood A²⁷27 Mehmood A, Yang S, Feng Z, et al. ATransfer Learning Approach for Early Diagnosis of Alzheimer's Disease on MRI Images. Neuroscience 2021;460:43-52. Doi: 10.1016/j.neuroscience.2021.01.002 https://doi.org/10.1016/j.neuroscience.2...	2021	low	low	low	low
Kang W²⁵25 Kang W, Lin L, Zhang B, Shen X, Wu SAlzheimer's Disease Neuroimaging Initiative. Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis. Comput Biol Med 2021; 136:104678. Doi: 10.1016/j.compbiomed.2021.104678 https://doi.org/10.1016/j.compbiomed.202...	2021	unclear	low	low	low
Hedayati R²⁴24 Hedayati R, Khedmati M, Taghipour-Gorjikolaie M. Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer's disease diagnosis. Biomed Signal Process Control 2021;66(03):102397. Doi: 10.1016/j.bspc.2020.102397 https://doi.org/10.1016/j.bspc.2020.1023...	2021	unclear	unclear	low	low
Akramifard H¹⁹19 Akramifard H, BalafarMA, Razavi SN, Ramli AR. Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders. J Med Signals Sens 2021;11(02):120-130. Doi: 10.4103/jmss.JMSS_11_20 https://doi.org/10.4103/jmss.JMSS_11_20...	2021	low	low	low	low
Suh C H²⁹29 Suh CH, Shim WH, Kim SJ, et al;Alzheimer's Disease Neuroimaging Initiative. Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images. AJNR Am J Neuroradiol 2020;41(12):2227-2234. Doi: 10.3174/ajnr.A6848 https://doi.org/10.3174/ajnr.A6848...	2020	low	low	low	low
Feng W²²22 Feng W, Halm-Lutterodt NV, Tang H, et al. Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process. Int J Neural Syst 2020;30(06):2050032. Doi: 10.1142/S012906572050032X https://doi.org/10.1142/S012906572050032...	2020	unclear	unclear	low	low
Wee C Y³¹31 Wee CY, Liu C, Lee A, Poh JS, Ji H, Qiu AAlzheimers Disease Neuroimage Initiative. Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. Neuroimage Clin 2019;23:101929. Doi: 10.1016/j.nicl.2019.101929 https://doi.org/10.1016/j.nicl.2019.1019...	2019	low	low	low	low
Oh K²⁸28 Oh K, Chung YC, Kim KW, Kim WS, Oh IS. Classification and Visualization of Alzheimer's Disease using Volumetric Convolutional Neural Network and Transfer Learning. Sci Rep 2019;9(01): 18150. Doi: 10.1038/s41598-019-54548-6 https://doi.org/10.1038/s41598-019-54548...	2019	unclear	low	low	low
Basaia S²⁰20 Basaia S, Agosta F, Wagner L, et al;Alzheimer's Disease Neuroimaging Initiative. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2019;21:101645. Doi: 10.1016/j.nicl.2018.101645 https://doi.org/10.1016/j.nicl.2018.1016...	2019	low	low	low	low

	T1-weighted MRI alone	Non-T1-weighted MRI alone
Number of studies	12	6
Combined sensitivity (95% CI)	0.84 (0.74, 0.91)	0.84 (0.72, 0.91)
Combined specificity (95% CI)	0.83 (0.73, 0.90)	0.9 (0.84, 0.94)
Positive likelihood ratio (95% CI)	5 (2.8, 8.9)	8.3 (5.0, 14.0)
Negative likelihood ratio (95% CI)	0.19 (0.10, 0.34)	0.18 (0.10, 0.33)
The combined diagnostic odds ratio (95% CI)	26 (9, 79)	46 (17, 127)
AUC	0.91 (0.88, 0.93)	0.93 (0.91, 0.95)

Academia Brasileira de Neurologia - ABNEURO R. Vergueiro, 1353 sl.1404 - Ed. Top Towers Offices Torre Norte, 04101-000 São Paulo SP Brazil, Tel.: +55 11 5084-9463 | +55 11 5083-3876 - São Paulo - SP - Brazil
E-mail: revista.arquivos@abneuro.org

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[1] Address for correspondence
Jie Li, (email: Jieli_btch@163.com).