ArticleDiagnostics (Basel, Switzerland)2020
Automatic Identification of Down Syndrome Using Facial Images with Deep Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 56 citations in OpenAlex.
- Deep Learning-Powered Down Syndrome Detection Using Facial Images.Life (Basel, Switzerland) · 2025Article
- Advancements in machine learning and biomarker integration for prenatal Down syndrome screening.Turkish journal of obstetrics and gynecology · 2025Article
- A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques.Life (Basel, Switzerland) · 2025Review
- Automated Multi-Class Facial Syndrome Classification Using Transfer Learning Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.BMC pediatrics · 2024Article
- Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis.Biomedicines · 2023Review
- Article
- Deep-learning approach to detect childhood glaucoma based on periocular photograph.Scientific reports · 2023Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- Three-dimensional facial scanner in the hands of patients: validation of a novel application on iPad/iPhone for three-dimensional imaging.Annals of translational medicine · 2021Article
- Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.BMC medical informatics and decision making · 2021Article
- Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks.Frontiers in pediatrics · 2021Article
- A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.BioMed research international · 2021Article
- Non-invasive health prediction from visually observable features.F1000Research · 2021Article
- Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss.Frontiers in genetics · 2021Article
- Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19.Sensors (Basel, Switzerland) · 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
6 authors at 1 institution in 1 country.
Funding
Abstract
Down syndrome is one of the most common genetic disorders. The distinctive facial features of Down syndrome provide an opportunity for automatic identification. Recent studies showed that facial recognition technologies have the capability to identify genetic disorders. However, there is a paucity of studies on the automatic identification of Down syndrome with facial recognition technologies, especially using deep convolutional neural networks. Here, we developed a Down syndrome identification method utilizing facial images and deep convolutional neural networks, which quantified the binary classification problem of distinguishing subjects with Down syndrome from healthy subjects based on unconstrained two-dimensional images. The network was trained in two main steps: First, we formed a general facial recognition network using a large-scale face identity database (10,562 subjects) and then trained (70%) and tested (30%) a dataset of 148 Down syndrome and 257 healthy images curated through public databases. In the final testing, the deep convolutional neural network achieved 95.87% accuracy, 93.18% recall, and 97.40% specificity in Down syndrome identification. Our findings indicate that the deep convolutional neural network has the potential to support the fast, accurate, and fully automatic identification of Down syndrome and could add considerable value to the future of precision medicine.
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.