التفاصيل البيبلوغرافية
| العنوان: |
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography |
| المؤلفون: |
Yoon Ah Do, Mijung Jang, Bo La Yun, Sung Ui Shin, Bohyoung Kim, Sun Mi Kim |
| المصدر: |
Diagnostics, Vol 11, Iss 8, p 1409 (2021) |
| بيانات النشر: |
MDPI AG, 2021. |
| سنة النشر: |
2021 |
| المجموعة: |
LCC:Medicine (General) |
| مصطلحات موضوعية: |
radiology, computer-aided diagnosis, breast cancer, artificial intelligence, mammography, diagnosis, Medicine (General), R5-920 |
| الوصف: |
The present study evaluated the diagnostic performance of artificial intelligence-based computer-aided diagnosis (AI-CAD) compared to that of dedicated breast radiologists in characterizing suspicious microcalcification on mammography. We retrospectively analyzed 435 unilateral mammographies from 420 patients (286 benign; 149 malignant) undergoing biopsy for suspicious microcalcification from June 2003 to November 2019. Commercial AI-CAD was applied to the mammography images, and malignancy scores were calculated. Diagnostic performance was compared between radiologists and AI-CAD using the area under the receiving operator characteristics curve (AUC). The AUCs of radiologists and AI-CAD were not significantly different (0.722 vs. 0.745, p = 0.393). The AUCs of the adjusted category were 0.726, 0.744, and 0.756 with cutoffs of 2%, 10%, and 38.03% for AI-CAD, respectively, which were all significantly higher than those for radiologists alone (all p < 0.05). None of the 27 cases downgraded to category 3 with a cutoff of 2% were confirmed as malignant on pathological analysis, suggesting that unnecessary biopsies could be avoided. Our findings suggest that the diagnostic performance of AI-CAD in characterizing suspicious microcalcification on mammography was similar to that of the radiologists, indicating that it may aid in making clinical decisions regarding the treatment of breast microcalcification. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2075-4418 |
| Relation: |
https://www.mdpi.com/2075-4418/11/8/1409; https://doaj.org/toc/2075-4418 |
| DOI: |
10.3390/diagnostics11081409 |
| URL الوصول: |
https://doaj.org/article/b47cc0c166e140b4a3615f3e2ff5250f |
| رقم الانضمام: |
edsdoj.b47cc0c166e140b4a3615f3e2ff5250f |
| قاعدة البيانات: |
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