Machine learning for treatment planning Artificial Intelligence is an umbrella term that covers all computational processes aimed at mimicking and extending human intelligence for problem-solving and decision-making. It is based on algorithms or arrays of mathematical formulae that make up specific computational learning methods. Machine learning and deep learning use algorithms in more complex ways to predict learned and new outcomes.
Its invasiveness, particularly in patients already at high clinical risk for bleeding complications, and its complexity and cost prevent its use as a routine screening technique. Similarly, ultrasound imaging is useful in detecting and classifying many liver disorders but requires skilled operators and interpretation.
For instance, liver fibrosis and CLD are common sequelae in chronic hepatitis B and chronic hepatitis C. Both these outcomes also pose independent risks for hepatocellular carcinoma . Diagnostic liver biopsies are impractical for screening all patients with chronic hepatitis, but ML-based screening offers rich promise.
Again, ultrasound-based ML algorithms outperformed experienced radiologists, yielding comparable information to CT or MRI for risk prediction and stratification of early HCC in cirrhosis patients and to differentiate benign from malignant tumors. DL-based automatic segmentation of liver tumors overcomes the time and skill constraints of manual segmentation. This improves tumor load evaluation and treatment planning.
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