Chaddad, Ahmad, Li, Jiali, Lu, Qizong, Li, Yujie, Okuwobi, Idowu Paul, Tanougast, Camel, Desrosiers, Christian et Niazi, Tamim.
2021.
« Can autism be diagnosed with artificial intelligence? A narrative review ».
Diagnostics, vol. 11, nº 11.
Compte des citations dans Scopus : 9.
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Résumé
Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Desrosiers, Christian |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 26 nov. 2021 19:19 |
| Dernière modification: | 16 oct. 2023 18:05 |
| URI: | https://espace2.etsmtl.ca/id/eprint/23616 |
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