WASHINGTON D.C. [USA]: According to a recent study, a new artificial intelligence technology can accurately identify rare genetic disorders using a photograph of a patient's face. Named DeepGestalt, the AI technology outperformed clinicians in identifying a range of syndromes in three trials and could add value in personalised care, CNN reported. The study was published in the journal Nature Medicine. According to the study, eight per cent of the population has disease with key genetic components and many may have recognisable facial features. The study further adds that the technology could identify, for example, Angelman syndrome, a disorder affecting the nervous system with characteristic features such as a wide mouth with widely spaced teeth etc. Speaking about it, Yaron Gurovich, the chief technology officer at FDNA and lead researcher of the study said, "It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large amount of conditions is great."
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online December 12 in the Journal of the American Medical Association.
A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online Dec. 12 in the Journal of the American Medical Association.
A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.