Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Feb 13;17(1):41.
doi: 10.1186/s13195-025-01686-x.

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning

Siyu Yang #  1   2 , Xintong Zhang #  3 , Xinyu Du  1 , Peng Yan  1 , Jing Zhang  1 , Wei Wang  2 , Jing Wang  4 , Lei Zhang  4   5   6   7 , Huaiqing Sun  1 , Yin Liu  1 , Xinran Xu  1 , Yaxuan Di  1 , Jin Zhong  1 , Caiyun Wu  1   8 , Jan D Reinhardt  9   10   11 , Yu Zheng  12 , Ting Wu  13
Affiliations

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning

Siyu Yang et al. Alzheimers Res Ther. .

Abstract

Background: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.

Methods: Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline within acceptable frames of reduction in overall model fit (AUC remaining > 0.8).

Results: A total of 607 individuals were included at baseline, of whom 538 participants were followed up at 12 months, 482 at 24 months, 268 at 36 months and 280 at 48 months. Predictive performance was excellent with AUCs ranging from 0.87 to 0.92 when all variable sets were considered. Parsimonious prediction models that still had a good performance with AUC 0.80-0.84 were established, each only including two variable sets. Neuropsychological outcomes were included in all parsimonious models. In addition, biomarker was included at year 1 and year 2, imaging data at year 3 and demographics at year 4. Under our pre-set threshold, the rate of upgrade to more intensive therapies according to predicted cognitive conversion was always higher than according to actual cognitive conversion so as to decrease the false positive rate, indicating the proportion of patients who would have missed upgraded treatment based on prognostic models although they actually needed it.

Conclusions: Neurophysiological tests combined with other indicator sets that vary along the AD continuum can improve can provide aid for clinical treatment decisions leading to improved management of the disease.

Trail registration information: ClinicalTrials.gov Identifier: NCT00106899 (Registration Date: 31 March 2005).

Keywords: Alzheimer’s disease; Cognitive conversion; Machine learning; Prediction model.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the institutional review boards of all participating institutions involved: Oregon Health and Science University; University of Southern California; University of California, San Diego; University of Michigan; Mayo Clinic, Rochester, MN, USA; Baylor College of Medicine; Columbia University; Washington University in St. Louis; University of Alabama-Birmingham; Mount Sinai School of Medicine; Rush University Medical Center; Wien Center; The Johns Hopkins University; University of South Florida Health Byrd Alzheimer’s Institute; New York University; Duke University Medical Center; University of Pennsylvania; University of Kentucky; University of Pittsburgh; University of Rochester Medical Center; University of California, Irvine; University of Texas Southwestern Medical Center; Emory University; University of Kansas; University of California, Los Angeles; Mayo Clinic, Jacksonville, FL, USA; Indiana University; Yale University School of Medicine; Jewish General Hospital/McGill University; Sunnybrook Health Sciences Centre; University of British Columbia; St. Joseph’s Hospital, Ontario, Canada; Northwestern University; Nathan S. Kline Institute for Psychiatric Research; Premiere Research Institute; University of California, San Francisco; Georgetown University; Brigham and Women’s Hospital; Stanford University; Banner Sun Health Research Institute; Boston University School of Medicine; Howard University; Case Western Reserve University; University of California, Davis; DENT Neurologic Institute; Parkwood Hospital; University of Wisconsin; University of California, Irvine Brain Imaging Center; Banner Alzheimer’s Institute; The Ohio State University; Albany Medical College; University of Iowa; Dartmouth-Hitchcock Medical Center; Wake Forest University Health Sciences Center; Rhode Island Hospital; Cornell Medical Center; Cleveland Clinic Lou Ruvo Center for Brain Health (CCLRBC); Roper St. Francis Hospital; and Butler Hospital Memory and Aging Program. The information on ethical approval and the centres involved in the ADNI study as listed above was obtained from the ADNI Data and Publications Committee. Written informed consent was obtained from all participants or their authorized representatives. Consent for publication: No applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Feature selection process in prediction of cognitive improvement. (a) 12 months; (b) 24 months; (c) 36 months; (d) 48 months. Notes: demographic data-d; genetic data-g; cognitive data-c; biomarker-b; imaging data-i
Fig. 2
Fig. 2
ROC comparisons of cognitive improvement at 12, 24, 36 and 48 months with best model fit. (a) 12 months; (b) 24 months; (c) 36 months; (d) 48 months
Fig. 3
Fig. 3
ROC comparisons of cognitive improvement at 12, 24, 36 and 48 months with parsimonious models. (a) 12 months; (b) 24 months; (c) 36 months; (d) 48 months
Fig. 4
Fig. 4
The AUCs with 84% CI of different models. (a) 12 months; (b) 24 months; (c) 36 months; (d) 48 months. Notes: demographic data-d; genetic data-g; cognitive data-c; biomarker-b; imaging data-i
Fig. 5
Fig. 5
Proposed treatment upgrade of best model fit following actual and predictive cognitive improvement at 12, 24, 36 and 48 months. (a) Treatment upgrade following actual cognitive improvement at 12-month; (b) Treatment upgrade following predictive cognitive improvement 12-month; (c) Treatment upgrade following actual cognitive improvement at 24-months; (d) Treatment upgrade following predictive cognitive improvement at 24-month; (e) Treatment upgrade following actual cognitive improvement at 36-month; (f) Treatment upgrade following predictive cognitive improvement at 36-month; (g) Treatment upgrade following actual cognitive improvement at 48-month; (h) Treatment upgrade following predictive cognitive improvement at 48-month
Fig. 6
Fig. 6
Proposed treatment upgrade of parsimonious models following actual and predictive cognitive improvement at 12, 24, 36 and 48 months. (a) Treatment upgrade following actual cognitive improvement at 12-month; (b) Treatment upgrade following predictive cognitive improvement 12-month; (c) Treatment upgrade following actual cognitive improvement at 24-months; (d) Treatment upgrade following predictive cognitive improvement at 24-month; (e) Treatment upgrade following actual cognitive improvement at 36-month; (f) Treatment upgrade following predictive cognitive improvement at 36-month; (g) Treatment upgrade following actual cognitive improvement at 48-month; (h) Treatment upgrade following predictive cognitive improvement at 48-month
See this image and copyright information in PMC

References

    1. Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement J Alzheimer’s Assoc. 2018;14(4):535–62. - PMC - PubMed
    1. Soldan A, Pettigrew C, Fagan AM, Schindler SE, Moghekar A, Fowler C, et al. ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes. Neurology. 2019;92(14):e1567–79. - PMC - PubMed
    1. Gao F, Lv X, Dai L, Wang Q, Wang P, Cheng Z et al. A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer’s dementia: China Aging and Neurodegenerative Initiative (CANDI) study. Alzheimers Dement. 2022. - PubMed
    1. Elmaleh DR, Farlow MR, Conti PS, Tompkins RG, Kundakovic L, Tanzi RE. Developing effective Alzheimer’s Disease therapies: clinical experience and future directions. J Alzheimers Dis. 2019;71(3):715–32. - PMC - PubMed
    1. Salehipour A, Bagheri M, Sabahi M, Dolatshahi M, Boche D. Combination therapy in Alzheimer’s Disease: is it time? J Alzheimers Dis. 2022;87(4):1433–49. - PubMed

Associated data

LinkOut - more resources