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RIN closely collaborates with The Biomedical Engineering and Imaging Institute at the Icahn School of Medicine at Mount Sinai

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of I...

Automated machine learning-based radiomics analysis versus deep learning-based classification for thyroid nodule on ultrasound images: a multi-center study

Often, the characteristics of thyroid nodules need to be determined by fine needle aspiration (FNA) biopsy. The increasing applications of machine learning and deep learning algorithms provide alternative noninvasive methods to study thyroid nodules on ultrasound images.

Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19.

RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning

To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning.

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