ReviewFrontiers in psychiatry2022
Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children.
Review in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Pooled it
- Special Education Teachers' Intention to Support the Use of Computerized Cognitive Training for Children With NDDs in China: A Theory of Planned Behavior Perspective.Journal of autism and developmental disorders · 2026Article
- Explainable AI for early developmental disability detection: a neuro-fuzzy approach.Frontiers in public health · 2026Article
- Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.Translational pediatrics · 2025Review
- Combined deep and reinforcement learning with gaming to promote healthcare in neurodevelopmental disorders: a new hypothesis.Frontiers in human neuroscience · 2025Article
- Article
- Aggregated Pattern Classification Method for improving neural disorder stage detection.Brain and behavior · 2024Article
- Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders.Frontiers in neuroscience · 2024Review
- Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.Frontiers in cell and developmental biology · 2024Review
- Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.Diagnostics (Basel, Switzerland) · 2023Article
- A social robot connected with chatGPT to improve cognitive functioning in ASD subjects.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevalence of neurodevelopment disorders (NDDs) among children has been on the rise. This has affected the health and social life of children. This condition has also imposed a huge economic burden on families and health care systems. Currently, it is difficult to perform early diagnosis of NDDs, which results in delayed intervention. For this reason, patients with NDDs have a prognosis. In recent years, machine learning (ML) technology, which integrates artificial intelligence technology and medicine, has been applied in the early detection and prediction of diseases based on data mining. This paper reviews the progress made in the application of ML in the diagnosis and treatment of NDDs in children based on supervised and unsupervised learning tools. The data reviewed here provide new perspectives on early diagnosis and treatment of NDDs.
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.