ArticleNPJ Parkinson's disease2023
A scoping review of neurodegenerative manifestations in explainable digital phenotyping.
Article in NPJ Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Activity and Behavioral Recognition Using Sensing Technology in Persons with Parkinson's Disease or Dementia: An Umbrella Review of the Literature.Sensors (Basel, Switzerland) · 2025Pooled it
- Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer.bioRxiv : the preprint server for biology · 2026Article
- Molecular Pathogenesis of Memory Impairment in Parkinson's Disease: An Exploration of Underlying Mechanisms.Health science reports · 2026Article
- AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.Alzheimer's research & therapy · 2026Review
- Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Screen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways.Biomedicines · 2026Article
- Video-based machine learning models for predicting deep brain stimulation outcomes in Parkinson's disease patients.NPJ Parkinson's disease · 2026Article
- Ethical considerations for multimodal artificial intelligence in healthcare.AI and ethics · 2026Article
- Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature Reduction.IEEE journal of translational engineering in health and medicine · 2026Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Improved Prediction of Activities of Daily Living from Wrist Electromyography Using Intermediate Gesture Classification.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A cross-language speech model for detection of Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- The Search for a Universal Treatment for Defined and Mixed Pathology Neurodegenerative Diseases.International journal of molecular sciences · 2024Review
- Machine learning explains response variability of deep brain stimulation on Parkinson's disease quality of life.NPJ digital medicine · 2024Article
- Talking about diseases; developing a model of patient and public-prioritised disease phenotypes.NPJ digital medicine · 2024Article
- Embryonic Zebrafish as a Model for Investigating the Interaction between Environmental Pollutants and Neurodegenerative Disorders.Biomedicines · 2024Review
- Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment.BMC medicine · 2024Article
- Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research.Journal of the American Heart Association · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
Identifiers
What Socratic 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 Socratic graph.