Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas

Key Points

• To assess the feasibility of an ultrasound radiomics model using machine learning for differentiating uterine fibroids from adenomyomas.
• Developed a model using ultrasound radiomics and support vector machine (SVM) analysis.
• Evaluated the model's effectiveness in differentiating between uterine fibroids and adenomyomas.
• Assessed the model's reliability as a noninvasive tool for clinical use.
• The ultrasound radiomics model effectively differentiated uterine fibroids from adenomyomas.
• The combined model demonstrated both reliability and effectiveness for clinical applications.