ArticleComputational and mathematical methods in medicine2022
Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 papers.
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
7 citing papers in PubMed, 80 citations in OpenAlex.
- Retraction Note: Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis.BMC psychiatry · 2025Article
- Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.Alcohol, clinical & experimental research · 2024Review
- Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study.Applied sciences (Basel, Switzerland) · 2024Article
- Article
- Retracted: Facial Features Detection System to Identify Children with Autism Spectrum Disorder: Deep Learning Models.Computational and mathematical methods in medicine · 2023Article
- Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework.Computational intelligence and neuroscience · 2023Article
- RETRACTED: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
- Retraction · 2023-11-01Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
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.