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AI model predicts risk for age-related macular degeneration

(HealthDay)—A new artificial intelligence algorithm can predict risk for age-related macular degeneration (AMD), according to a study published in the April issue of Translational Vision Science and Technology.

Alauddin Bhuiyan, Ph.D., from iHealthScreen Inc. in New York City, and colleagues used 116,875 color fundus photos from 4,139 participants of the Age-related Eye Disease Study to develop a machine learning technique that can predict risk for progression to late AMD within one or two years. This model, which includes sociodemographic and , was validated using data from the Nutritional AMD Treatment-2 (NAT-2) study.

The researchers found that for identification of early/none versus intermediate/late (e.g., referral level) AMD, the model achieved 99.2 percent accuracy. Overall, for a two-year incidence of late AMD (any), the prediction achieved 86.36 percent accuracy, with 66.88 percent for late dry AMD and 67.15 percent for late wet AMD. Using data from the NAT-2 study, the two-year late AMD prediction accuracy was 84 percent.

“Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment,” the authors write.

Incidence of early-onset gastric cancer increasing in the U.S.

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Nonketotic hyperglycinemia – Genetics Home Reference

  • Aliefendioğlu D, Tana Aslan Ay, Coşkun T, Dursun A, Cakmak FN, Kesimer M. Transient nonketotic hyperglycinemia: two case reports and literature review. Pediatr Neurol. 2003 Feb;28(2):151-5. Review.

  • Applegarth DA, Toone JR. Glycine encephalopathy (nonketotic hyperglycinaemia) : review and update. J Inherit Metab Dis. 2004;27(3):417-22. Review.

  • Applegarth DA, Toone JR. Glycine encephalopathy (nonketotic hyperglycinemia): comments and speculations. Am J Med Genet A. 2006 Jan 15;140(2):186-8.

  • Coughlin CR 2nd, Swanson MA, Kronquist K, Acquaviva C, Hutchin T, Rodríguez-Pombo P, Väisänen ML, Spector E, Creadon-Swindell G, Brás-Goldberg AM, Rahikkala E, Moilanen JS, Mahieu V, Matthijs G, Bravo-Alonso I, Pérez-Cerdá C, Ugarte M, Vianey-Saban C, Scharer GH, Van Hove JL. The genetic basis of classic nonketotic hyperglycinemia due to mutations in GLDC and AMT. Genet Med. 2017 Jan;19(1):104-111. doi: 10.1038/gim.2016.74. Epub 2016 Jun 30. Erratum in: Genet Med. 2018 Jan 04;:.

  • Dinopoulos A, Matsubara Y, Kure S. Atypical variants of nonketotic hyperglycinemia. Mol Genet Metab. 2005 Sep-Oct;86(1-2):61-9. Review.

  • Hoover-Fong JE, Shah S, Van Hove JL, Applegarth D, Toone J, Hamosh A. Natural history of nonketotic hyperglycinemia in 65 patients. Neurology. 2004 Nov 23;63(10):1847-53.

  • Kikuchi G, Motokawa Y, Yoshida T, Hiraga K. Glycine cleavage system: reaction mechanism, physiological significance, and hyperglycinemia. Proc Jpn Acad Ser B Phys Biol Sci. 2008;84(7):246-63. Review.

  • Stence NV, Fenton LZ, Levek C, Tong S, Coughlin CR 2nd, Hennermann JB, Wortmann SB, Van Hove JLK. Brain imaging in classic nonketotic hyperglycinemia: Quantitative analysis and relation to phenotype. J Inherit Metab Dis. 2019 May;42(3):438-450. doi: 10.1002/jimd.12072. Epub 2019 Mar 20.

  • Swanson MA, Coughlin CR Jr, Scharer GH, Szerlong HJ, Bjoraker KJ, Spector EB, Creadon-Swindell G, Mahieu V, Matthijs G, Hennermann JB, Applegarth DA, Toone JR, Tong S, Williams K, Van Hove JL. Biochemical and molecular predictors for prognosis in nonketotic hyperglycinemia. Ann Neurol. 2015 Oct;78(4):606-18. doi: 10.1002/ana.24485. Epub 2015 Aug 10. Erratum

  • Health Life

    Gender imbalanced datasets may affect the performance of AI pathology classification

    Credit: CC0 Public Domain

    A team of researchers from Universidad Nacional del Litoral–Consejo Nacional de Investigaciones Cient´ıficas and Universidad Nacional de Entre R´ııos, both in Argentina, has found evidence of gender-imbalanced datasets affecting the performance of pathology classification with AI-based diagnostic systems. In their paper published in Proceedings of the National Academy of Sciences, the group describes testing three open-source machine algorithms used for analyzing X-ray images to detect various medical conditions, and what they found.

    Though it may not be , AI systems are currently being used in a wide variety of commercial applications, including article selection on news and , which movies get made,and maps that appear on our phones—AI systems have become trusted tools by big business. But their use has not always been without controversy. In recent years, researchers have found that AI apps used to approve mortgage and other loan applications are biased, for example, in favor of white males. This, researchers found, was because the dataset used to train the system mostly comprised white male profiles. In this new effort, the researchers wondered if the same might be true for AI systems used to assist doctors in diagnosing patients.

    The work involved evaluating three open-source AI systems that are still in the experimental stage. Each was trained on chest X-rays obtained from NIH and Stanford University databases, both of which contained slightly more male profiles. To find out if the systems would produce biased results, the researchers skewed the data in various ways. In some cases, they used primarily male profiles, in others primarily female.

    In looking at their results, the researchers found that there was a definite bias—when the data was mostly male, the error rates for processing female profiles rose. The same was true if the

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    National Health Interview Survey 2017

    According to data from the 2017 National Health Interview Survey (NHIS) released in November 2018, the number of American adults and children using yoga and meditation has significantly increased over previous years and the use of chiropractic has increased modestly for adults and held steady for children.

    See Press Release: More adults and children are using yoga and meditation: Nationwide survey reveals significant increases

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