ArticleFrontiers in medicine2023
Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence-Based Approach for Automated Gonad Volume Quantification Using Magnetic Resonance Imaging in Healthy Adolescents Across Puberty.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
12 authors.
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Abstract
Background: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Purpose: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. Materials and methods: The study cohort consisted of retrospectively collected patient data ( Results: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV ( Conclusion: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.
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