Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease
The integration of deep learning into image processing and interpretation is becoming increasingly prevalent in clinical practice. In our pilot study, we conducted a comparative analysis of the performance of a deep learning model alongside corresponding axial- and coronal-section models, a comparison that has been less explored in prior research. Our findings suggest that our deep learning model for TKV measurement performs comparably to medical professionals. However, we observed that varying image orientations could introduce measurement bias. Specifically, our AI model exhibited superior performance with axial-section images compared to coronal-section images.
Authors
- Wen‐Shuo Wu (ORCID: https://orcid.org/0000-0002-6259-3409)
- Pei‐Lun Chu (ORCID: https://orcid.org/0000-0001-8057-5661)
- Jia-Lien Hsu (ORCID: https://orcid.org/0000-0002-4818-3198)
- Anandakumar Singaravelan
- Chih-Yun Lai
- Zhilin Li (ORCID: https://orcid.org/0000-0003-2867-2848)
- Chia–Nan Lin
- Tze‐Wah Kao
Institutions
- Fu Jen Catholic University (TW)
Publication Details
- Journal
- Bioengineering
- Published
- 2024-09-26
- DOI
- https://doi.org/10.3390/bioengineering11100963
- Citations
- 5
- Primary Topic
- Genetic and Kidney Cyst Diseases
- Type
- article
- Field-Weighted Citation Impact
- 2.75