Publications by authors named "S K Matta"

The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.

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Article Synopsis
  • AI improves the accuracy of lung nodule detection in chest X-rays (CXRs), with a notable increase in sensitivity and area-under-the-curve values when AI is used as a second reader.
  • The study involved 300 CXRs from various hospitals, where both radiologists and non-radiology physicians assessed the images once without and once with AI assistance.
  • Results showed a significant improvement in detecting nodules (sensitivity increased from 72.8% to 83.5%) while maintaining a similar level of specificity with and without AI.
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The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss the diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.

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Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these models effectively handle domain shift? This question is crucial to limit DL models performance degradation. Medical data are dynamic and prone to domain shift, due to multiple factors.

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Motility allows microbes to explore and maximize success in their environment; however, many laboratory bacterial strains have a reduced or altered capacity for motility. Swimming motility in depends on peritrichous flagella and is carried out individually as cells move by biased random walks toward attractants. Previously, we adapted strain 3610 to the laboratory for 300 generations in lysogeny broth (LB) batch culture and isolated lab-adapted strains.

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