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Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Background

Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruction for detection of urolithiasis by unenhanced CT in children and young adults.

Methods

This was an IRB approved retrospective study involving post-hoc reconstruction of clinically acquired unenhanced abdomen/pelvis CT scans. Images were reconstructed with six different manufacturer-standard DLR algorithms and reformatted in 3 planes (axial, sagittal, and coronal) at 3 mm intervals. De-identified reconstructions were loaded as independent examinations for review by 3 blinded radiologists (R1, R2, R3) tasked with identifying and measuring all stones. Results were compared to the clinical iterative reconstruction images as a reference standard. IntraClass correlation coefficients and kappa (k) statistics were used to quantify agreement.

Results

CT data for 14 patients (mean age: 17.3 ± 3.4 years, 5 males and 9 females, weight class: 31-70 kg (n = 6), 71-100 kg (n = 7), > 100 kg (n = 1)) were reconstructed into 84 total exams. 7 patients had urinary tract calculi. Interobserver agreement on the presence of any urinary tract calculus was substantial to almost perfect (k = 0.71–1) for all DLR algorithms. Agreement with the reference standard on number of calculi was excellent (ICC = 0.78–0.96) and agreement on the size of the largest calculus was fair to excellent (ICC = 0.51–0.97) depending on reviewer and DLR algorithm.

Conclusion

Deep learning reconstruction of unenhanced CT images allows similar renal stone detectability compared to iterative reconstruction.

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Authors and Affiliations

  1. Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 5031, Cincinnati, OH, 45229, USA

    Samjhana Thapaliya, Samuel L. Brady, Elanchezhian Somasundaram, Christopher G. Anton, Brian D. Coley, Alexander J. Towbin, Jonathan R. Dillman & Andrew T. Trout

  2. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Samuel L. Brady, Elanchezhian Somasundaram, Christopher G. Anton, Brian D. Coley, Alexander J. Towbin, Jonathan R. Dillman & Andrew T. Trout

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Brian D. Coley, Alexander J. Towbin, Bin Zhang & Andrew T. Trout

  4. Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Bin Zhang

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  1. Samjhana Thapaliya
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Correspondence to Andrew T. Trout.

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Thapaliya, S., Brady, S.L., Somasundaram, E. et al. Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms. Abdom Radiol 47, 265–271 (2022). https://doi.org/10.1007/s00261-021-03274-7

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  • DOI: https://doi.org/10.1007/s00261-021-03274-7

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