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Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study.
Bone Rep
March 2025
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America.
High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site.
View Article and Find Full Text PDFRelationship of Biochemical and Sonographic Markers with Disease Severity in Rosacea Patients Without Cardiovascular Disease.
Life (Basel)
January 2025
Department of Dermatology, New York Medical College, New York, NY 10595, USA.
Rosacea is a chronic inflammatory skin disorder characterized by central facial redness, papulopustular lesions, and occasionally phymatous changes. There is ongoing debate regarding rosacea as a cutaneous disease with systemic inflammatory effects and its associations with cardiovascular diseases. Although the pathogenesis of both atherosclerosis and rosacea demonstrate notable similarities, particularly in the central role of inflammation, significant gaps in understanding these connections remain.
View Article and Find Full Text PDFArtificial Intelligence in Fetal and Pediatric Echocardiography.
Children (Basel)
December 2024
Division of Pediatric Cardiology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.
Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements.
View Article and Find Full Text PDFIntra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development.
Ultrasound Obstet Gynecol
January 2025
Robinson Research Institute, University of Adelaide, Adelaide, Australia.
Objectives: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development.
Methods: A proposed scoring system to quantify TVS image quality was created following a review of the literature.
Hybrid remote and in-clinic maternal-fetal surveillance for women with gestational diabetes: A prospective pilot study.
Int J Gynaecol Obstet
January 2025
The Josef Buchmann Gynecology and Maternity Center, Sheba Medical Center, Tel Hashomer, Israel.
Objective: This study explores a hybrid approach to maternal-fetal care for gestational diabetes (GD), integrating virtual visits seamlessly with in-clinic assessments. We assessed the feasibility, time efficiency, patient satisfaction, and clinical outcomes to facilitate wider adoption of maternal-fetal telemedicine.
Methods: We conducted a 4-week prospective study involving 20 women with GD at ≥32 weeks of pregnancy, alternating between remote and in-clinic weekly visits.
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