Artificial intelligence (AI) is being increasingly used in medical ultrasound imaging to assist radiologists and sonographers. Various AI algorithms are being developed that can analyze ultrasound images and provide objective measurements and diagnostic insights. This helps improve accuracy and allows ultrasound to be used for a wider range of clinical applications. Some key uses of AI in ultrasound include:
Disease detection and diagnosis - Deep learning networks are training on large datasets of ultrasound scans along with diagnoses to detect and diagnose various diseases. This includes identifying breast lumps, measuring fetal growth, detecting gallstones or kidney stones, among others. The AI systems can then review new scans and provide a probable diagnosis automatically. This acts as a valuable second opinion for clinicians.
Anatomical structure identification - Identifying various anatomical structures like organs, blood vessels in ultrasound scans can sometimes be challenging. AI is helping overcome this by using deep learning to automatically annotate where different structures are located in the images. This assists in navigation and understanding complicated scans more easily.
Measurement and biometry - AI in Ultrasound Imaging enables automatic measurement of various parameters from ultrasound scans like fetal head circumference, tumor size, heart chambers, intima-media thickness and more. Such objective quantitative readings assist clinicians in tracking progress or detecting abnormalities.
Elastography analysis - Elastography ultrasound techniques map tissue stiffness to identify lesions. AI helps analyze these scans and provides qualitative and quantitative readings of lesions and surrounding tissues, improving cancer detection.
Reduction of user variability - One challenge with ultrasound is user dependence, as readings can vary with technician experience and scanning skills. AI helps minimize such variability by providing standardized, objective analysis regardless of user. This promotes more consistent diagnoses.
Artificial Intelligence in Ultrasound Imaging Applications Bring Accuracy Benefits
Various studies have shown AI applications can improve accuracy of ultrasound readings compared to experienced clinicians:
- A 2020 study using AI to detect gallstones from ultrasound found a 95.2% accuracy vs 83.4% for radiologists. This reduced false negatives by 52.6%.
- For fetal ultrasound scans, AI models have been reported to estimate head circumference, femur length and abdominal circumference within 0.5-1mm of manual readings, improving accuracy and consistency.
- In breast ultrasound, deep learning helped detect cancers with over 90% sensitivity and specificity in some studies versus up to 30% being initially missed by radiologists.
- For cardiac ultrasound, AI achieved over 95% accuracy in measurements like ejection fraction compared to typical 3-5% error range for manual readings.
- Researchers also combined ultrasound and AI to identify liver lesions, showing improved detection over dual radiologist readings alone.
Such consistent, highly accurate readings allow clinicians to leverage ultrasound for a far greater number of clinical scenarios than before. This improves patient access to care. AI also acts as a valuable supplement rather than replacement for human experts.
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