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Artificial Intelligence in Adrenal Imaging
Daniel I Glazer1, Bernardo C Bizzo2, David T Fuentes3
1Department of Radiology, Harvard Medical School, Boston, MA, USA; Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
Magnetic Resonance Imaging Clinics of North America
|August 11, 2026
Summary
Artificial intelligence (AI) can help radiologists identify adrenal masses and determine their cause. This review covers AI terminology and methods for adrenal gland segmentation, lesion detection, and characterization.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Adrenal lesions are common, affecting about 5% of people.
- Distinguishing significant from insignificant adrenal lesions is difficult due to overlapping imaging features.
- Artificial intelligence (AI) shows promise in assisting radiologists with adrenal mass identification and etiological diagnosis.
Purpose of the Study:
- To define common AI terminology relevant to adrenal imaging.
- To summarize AI-based techniques for adrenal gland segmentation.
- To review AI methods for adrenal lesion detection and characterization.
Main Methods:
- Literature review of AI applications in adrenal imaging.
- Discussion of AI techniques including segmentation, detection, and characterization algorithms.
- Explanation of AI terminology and concepts.
Main Results:
- AI offers potential solutions for differentiating adrenal lesions.
- AI techniques can automate and improve the accuracy of adrenal gland segmentation.
- AI methods are being developed for robust adrenal lesion detection and classification.
Conclusions:
- AI holds significant potential to enhance the diagnostic capabilities of radiologists in evaluating adrenal lesions.
- Standardized AI terminology is crucial for effective communication and development in this field.
- Further research and validation of AI tools are needed for clinical integration in adrenal imaging.
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