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Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images:

Liang Yongping¹, Ping Zhou², Yang Wang¹

¹Department of Ultrasound, Tangshan People's Hospital, Tangshan, China.

JMIR Medical Informatics

|July 3, 2026

Summary

The S-Detect computer-aided diagnostic system demonstrates high intrasystem repeatability for breast nodule classification on static ultrasound images, indicating strong software consistency. Further validation is needed for real-world clinical reproducibility.

Keywords:

S‑Detect breast cancer computer‑aided diagnosis repeatability ultrasonography

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Breast Imaging

Background:

Computer-aided diagnostic (CADx) systems like S-Detect are increasingly used in breast ultrasound.
Previous studies focused on S-Detect's diagnostic accuracy, but its software-level repeatability with identical images was not systematically evaluated.

Purpose of the Study:

To assess the intrasystem repeatability of the S-Detect CADx system for classifying breast nodules using identical static ultrasound images.

Main Methods:

A retrospective, blinded repeatability study analyzed 398 breast nodules from 261 women.
Identical static ultrasound images were analyzed twice by S-Detect on the same workstation under blinded conditions.
Repeatability was measured using concordance rates and Cohen's kappa, with comparisons to surgical histopathology.

Main Results:

The overall concordance rate between the two S-Detect analyses was 98.2% (Cohen's κ = 0.95).
A small percentage (4.5%) of benign nodules were reclassified as malignant on repeat analysis; no malignant-to-benign changes occurred.
Diagnostic performance remained stable across the two analysis runs (AUC 0.913 vs 0.902).

Conclusions:

S-Detect exhibits high intrasystem repeatability for classifying breast nodules on static images, suggesting robust software consistency.
While software consistency is high, its real-world clinical reproducibility requires additional investigation and validation.