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Article
| Open Access
Automated multilabel diagnosis on electrocardiographic images and signals
The application of artificial intelligence for automated diagnosis of electrocardiograms can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. Here, the authors report the development of a multi-label automated diagnosis model for electrocardiographic images.
- Veer Sangha
- , Bobak J. Mortazavi
- & Rohan Khera
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Article
| Open Access
Automatic diagnosis of the 12-lead ECG using a deep neural network
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
- Antônio H. Ribeiro
- , Manoel Horta Ribeiro
- & Antonio Luiz P. Ribeiro
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Article
| Open Access
A small number of abnormal brain connections predicts adult autism spectrum disorder
Autism spectrum disorder (ASD) is manifested by subtle but significant changes in the brain. Here, Yahata and colleagues devise a novel machine learning algorithm and develop a reliable ASD classifier based on brain functional connectivity, with which they quantitatively measure neuroimaging dimensions between ASD and other mental disorders.
- Noriaki Yahata
- , Jun Morimoto
- & Mitsuo Kawato
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Impedance sensing device enables early detection of pressure ulcers in vivo
Sustained pressure on the skin reduces blood flow and causes wounds. Here the authors describe a flexible electronic ‘bandage’ that measures changes in tissue impedance spectra and detects early tissue damage in rats before it can be visualized, thus enabling possible prevention of pressure ulcers.
- Sarah L. Swisher
- , Monica C. Lin
- & Michel M. Maharbiz
Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts