Effectiveness of Embolization for Pulmonary Arteriovenous Malformations from Distal of the Last Normal Branch of the Pulmonary Artery.

Purpose: This retrospective study of patients with pulmonary arteriovenous malformations aims to assess the efficacy of embolization distal to the origin of the last normal branch of the pulmonary artery.

Material And Methods: A total of 30 consecutive patients with 38 untreated pulmonary arteriovenous malformations underwent coil embolization distal to the origin of the last normal branch of the pulmonary artery between September 2015 and October 2021. The median (interquartile range) age of patients (5 males, 25 females) was 59 years (50-68 years old), and the median (interquartile range) sizes of the feeding artery and sac were 2.

Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

Background: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).

Methods: We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features.

Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020.

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value.

Laxative use and mortality in patients on haemodialysis: a prospective cohort study.

Background: Patients on haemodialysis (HD) are often constipated. This study aimed to assess the relationship between constipation and mortality in such patients. In this study, constipation was defined as receiving prescription laxatives, based on the investigation results of "a need to take laxatives is the most common conception of constipation" reported by the World Gastroenterology Organization Global Guidelines.