Optimizing Artificial Intelligence Thresholds for Mammographic Lesion Detection: A Retrospective Study on Diagnostic Performance and Radiologist–Artificial Intelligence Discordance
<b>Background/Objectives:</b> Artificial intelligence (AI)-based systems are increasingly being used to assist radiologists in detecting breast cancer on mammograms. However, applying fixed AI score thresholds across diverse lesion types may compromise diagnostic performance, especially...
Saved in:
| Main Authors: | Taesun Han, Hyesun Yun, Young Keun Sur, Heeboong Park |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/11/1368 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Results of Two-Years Mammographic Screening in the Kaluga Region
by: K. S. Drzhevetskaya, et al.
Published: (2022-10-01) -
Stratifying Breast Lesion Risk Using BI-RADS: A Correlative Study of Imaging and Histopathology
by: Sebastian Ciurescu, et al.
Published: (2025-07-01) -
The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists
by: Agata Król, et al.
Published: (2025-07-01) -
The association between mammography reports and women’s age using retrospective data form a medical system
by: Wen-Mi Chang, et al.
Published: (2025-06-01) -
Diagnostic value of BI-RADS categories in the management of patients with benign breast pathology
by: G. P. Korzhenkova
Published: (2017-02-01)