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.

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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
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article

Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease

Wen‐Shuo Wu, Pei‐Lun Chu, Jia-Lien Hsu, Anandakumar Singaravelan et al.
5 citations
Bioengineering
Genetic and Kidney Cyst Diseases
2.75
article

Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease

Wen‐Shuo Wu, Pei‐Lun Chu, Jia-Lien Hsu, Anandakumar Singaravelan, Chih-Yun Lai, Zhilin Li, Chia–Nan Lin, Tze‐Wah Kao
article en
5 citations

Abstract

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.

BioengineeringVol. 11(10)
Fu Jen Catholic University (TW)
Openalex Percentile: Top 9%
Genetic and Kidney Cyst Diseases
2.75
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