Prediction of anemia with thoracic computed tomography findings.

Background: This study explored the potential of non-contrast thoracic computed tomography (CT) to predict anemia by correlating CT parameters with hemoglobin (Hb) levels in patients who underwent non-contrast thoracic CT for various indications.

Methods: This retrospective study included 150 patients who underwent non-contrast thoracic CT scans and complete blood counts within 24 hours at our center between January and June 2023. Exclusion criteria included acute bleeding, iron accumulation disorders, recent transfusions, pregnancy, and certain thoracic CT artifacts.

JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels.

The demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discerning test strategies. The aim of this study is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in cases of erythrocytosis. Test results were sourced from samples sent to a tertiary hospital's genetic laboratory between 2017 and 2023, meeting 2016 World Health Organization criteria for JAK2V617F mutation testing.

Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis.

Background: This study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features.

Methods: Retrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023.