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Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.
Br J Radiol
January 2025
2nd Department of Radiology, University General Hospital "ATTIKON", Medical School, National and Kapodistrian University of Athens, Greece.
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted.
View Article and Find Full Text PDFManagement factors affecting milk yield, composition, and quality on smallholder dairy farms.
Trop Anim Health Prod
January 2025
Department of Clinical Sciences, Swedish University of Agricultural Sciences, Box 7024, 750 07, Uppsala, Sweden.
A cross-sectional study on 156 smallholder dairy farms in Rwanda was carried out to assess the association between farm management practices and milk yield and quality. A pre-tested questionnaire was used to collect data on cow characteristics and farm management practices. Milk yield was recorded at household level, milk composition was monitored using a Lactoscan device (Milk Analyzer).
View Article and Find Full Text PDFA phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.
NPJ Digit Med
January 2025
Department of Computer Science and Technology & Institute for Artificial Intelligence & BNRist, Tsinghua University, Beijing, China.
Rare diseases, affecting ~350 million people worldwide, pose significant challenges in clinical diagnosis due to the lack of experienced physicians and the complexity of differentiating between numerous rare diseases. To address these challenges, we introduce PhenoBrain, a fully automated artificial intelligence pipeline. PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases.
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