Exploration of the Application of Multimodal Feature Analysis Based on Random Forest Algorithm Combining Ultrasound Elastography and Contrast-Enhanced Ultrasound in the Diagnosis of Ovarian Tumors

Key Points

• Patients diagnosed with ovarian tumors showed accurate classification using the random forest algorithm.
• Sensitivity reached 92% and specificity was 88%, indicating strong performance of the approach.
• Multimodal feature analysis involving ultrasound elastography and contrast-enhanced ultrasound was applied.
• These findings may enhance diagnostic procedures for ovarian tumors, promoting better patient outcomes.