| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Qing Wu, Eric M Morrow, Ece D Gamsiz Uzu. A deep learning model for prediction of autism status using whole-exome sequencing data. PLoS computational biology. vol 20. issue 11. 2024-11-08. PMID:39514604. |
machine learning (ml), including deep learning (dl), has been evaluated in phenotype prediction, but this method has been limited in its application to autism. |
2024-11-08 |
2024-11-16 |
Not clear |
| Qing Wu, Eric M Morrow, Ece D Gamsiz Uzu. A deep learning model for prediction of autism status using whole-exome sequencing data. PLoS computational biology. vol 20. issue 11. 2024-11-08. PMID:39514604. |
we developed a dl model, the separate translated autism research neural network (star-nn) model to predict autism status. |
2024-11-08 |
2024-11-16 |
Not clear |
| Yang Ding, Heng Zhang, Ting Qi. Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis. BMC psychiatry. vol 24. issue 1. 2024-10-29. PMID:39468522. |
the use of the deep learning (dl) approach has been suggested or applied to identify childhood autism spectrum disorder (asd). |
2024-10-29 |
2024-10-31 |
Not clear |
| Mélanie Garcia, Clare Kell. 3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data. PloS one. vol 19. issue 10. 2024-10-21. PMID:39432512. |
we trained 3d dl models to predict autism diagnosis using the openly available abide i and ii datasets (n = 1329, split into training, validation, and test sets). |
2024-10-21 |
2024-10-24 |
Not clear |
| Mélanie Garcia, Clare Kell. 3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data. PloS one. vol 19. issue 10. 2024-10-21. PMID:39432512. |
here, we address these challenges and describe an interpretable predictive pipeline for inferring autism diagnosis using 3d dl applied to minimally processed structural mri scans. |
2024-10-21 |
2024-10-24 |
Not clear |
| Benoit Dufumier, Pietro Gori, Sara Petiton, Robin Louiset, Jean-François Mangin, Antoine Grigis, Edouard Duchesna. Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry. NeuroImage. 2024-06-07. PMID:38848981. |
this paper extensively compares dl and sml prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and autism spectrum disorder (asd) diagnosis. |
2024-06-07 |
2024-06-10 |
Not clear |
| Xiang Guo, Jiehuan Wang, Xiaoqiang Wang, Wenjing Liu, Hao Yu, Li Xu, Hengyan Li, Jiangfen Wu, Mengxing Dong, Weixiong Tan, Weijian Chen, Yunjun Yang, Yueqin Che. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. European radiology. 2021-11-25. PMID:34482428. |
to develop and validate deep learning (dl) methods for diagnosing autism spectrum disorder (asd) based on conventional mri (cmri) and apparent diffusion coefficient (adc) images. |
2021-11-25 |
2023-08-13 |
Not clear |
| Ray O Bahado-Singh, Sangeetha Vishweswaraiah, Buket Aydas, Uppala Radhakrishn. Placental DNA methylation changes and the early prediction of autism in full-term newborns. PloS one. vol 16. issue 7. 2021-11-02. PMID:34260616. |
dl yielded an auc (95% ci) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. |
2021-11-02 |
2023-08-13 |
human |
| Ray O Bahado-Singh, Sangeetha Vishweswaraiah, Buket Aydas, Uppala Radhakrishn. Placental DNA methylation changes and the early prediction of autism in full-term newborns. PloS one. vol 16. issue 7. 2021-11-02. PMID:34260616. |
six artificial intelligence (ai) algorithms including deep learning (dl) to determine the predictive accuracy of cpg markers for autism detection. |
2021-11-02 |
2023-08-13 |
human |
| Zhao Zhang, Guangfei Li, Yong Xu, Xiaoying Tan. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel, Switzerland). vol 11. issue 8. 2021-08-30. PMID:34441336. |
then, the application of ml and dl methods to six typical neurological and psychiatric diseases is summarized, including alzheimer's disease (ad), parkinson's disease (pd), major depressive disorder (mdd), schizophrenia (scz), attention-deficit/hyperactivity disorder (adhd), and autism spectrum disorder (asd). |
2021-08-30 |
2023-08-13 |
human |
| Ray O Bahado-Singh, Sangeetha Vishweswaraiah, Buket Aydas, Nitish K Mishra, Ali Yilmaz, Chittibabu Guda, Uppala Radhakrishn. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain research. vol 1724. 2020-10-29. PMID:31521637. |
dl yielded an auc (95% ci) = 1.00 (0.80-1.00) with 97.5% sensitivity and 100.0% specificity for autism detection. |
2020-10-29 |
2023-08-13 |
Not clear |
| Ray O Bahado-Singh, Sangeetha Vishweswaraiah, Buket Aydas, Nitish K Mishra, Ali Yilmaz, Chittibabu Guda, Uppala Radhakrishn. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain research. vol 1724. 2020-10-29. PMID:31521637. |
the accuracy of cytosine methylation for autism detection using six different machine learning/artificial intelligence (ai) approaches including deep-learning (dl) was determined. |
2020-10-29 |
2023-08-13 |
Not clear |