ArticleJournal of clinical medicine2023
A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.Journal of medical Internet research · 2026Article
- Diagnosis and follow-up of the first case of Gaucher disease under enzyme replacement therapy in Senegal.Ghana medical journal · 2026Article
- Development and Validation of a Modality-Invariant 3D Swin U-Net Transformer for Liver and Spleen Segmentation on Multi-Site Clinical Bi-parametric MR Images.Journal of imaging informatics in medicine · 2025Article
- Development and validation of an interpretable machine learning model for standard spleen volume prediction.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of gravity effect on liver and spleen volumes using multiposture MRI.Radiological physics and technology · 2025Article
- Model-Assisted Spleen Contouring for Assessing Splenomegaly in Myelofibrosis: A Fast and Reproducible Approach to Evaluate Progression and Treatment Response.Journal of clinical medicine · 2025Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.