ArticleJournal of acquired immune deficiency syndromes (1999)2019
Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.
Article in Journal of acquired immune deficiency syndromes (1999), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study.The Clinical neuropsychologist · 2026Article
- Connectome-based predictive modelling predicts frailty levels in older adults.bioRxiv : the preprint server for biology · 2025Article
- Connectome-based predictive modeling of grip strength: a marker of physical frailty.Frontiers in neuroscience · 2025Article
- Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.The Journal of infectious diseases · 2023Article
- The Structural and Functional Correlates of Frailty in Persons With Human Immunodeficiency Virus.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Co-Infection and Cancer: Host-Pathogen Interaction between Dendritic Cells and HIV-1, HTLV-1, and Other Oncogenic Viruses.Viruses · 2022Review
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Neuroimaging the Neuropathogenesis of HIV.Current HIV/AIDS reports · 2021Review
- Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.Journal of acquired immune deficiency syndromes (1999) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDeep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors.
settingVirologically suppressed (<50 copies/mL) PLWH (n = 125) on combination antiretroviral therapy were enrolled. Participants averaged 51.4 (11.4) years of age and 13.7 (2.8) years of education. Participants were administered a neuropsychological battery, assessed for frailty, and completed structural neuroimaging.
methodsDeep neural network (DNN) models were trained to classify PLWH as cognitively unimpaired or impaired based on neuropsychological tests (Hopkins Verbal Learning Test-Revised and Brief Visuospatial Memory Test-Revised, Trail making, Letter-Number Sequencing, Verbal Fluency, and Color Word Interference), as well as frail, prefrail, or nonfrail based on the Fried phenotype criteria (at least 3 of the following 5: weight loss, physical inactivity, exhaustion, grip strength, walking time).
resultsDNNs classified individuals with cognitive impairment in the learning, memory, and executive domains with 82%-86% accuracy (0.81-0.87 AUC). Our model classified nonfrail, prefrail, and frail PLWH with 75% accuracy. The strongest predictors of cognitive impairment were cortical (parietal, occipital, and temporal) and subcortical (amygdala, caudate, and hippocampus) regions, whereas the strongest predictors of frailty were subcortical (amygdala, caudate, hippocampus, thalamus, pallidum, and cerebellum).
conclusionsDNN models achieved high accuracy in classifying cognitive impairment and frailty status in PLWH. Feature selection algorithms identified predictive regions in each domain and identified overlapping regions between cognitive impairment and frailty. Our results suggest frailty in HIV is primarily subcortical, whereas cognitive impairment in HIV involves subcortical and cortical brain regions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.