An algorithm for cognitive fusion targeted tumor puncture based on 3-D mathematical modelling.

Area of Science:

• Medical Imaging and Interventional Radiology
• Computational Biology and Mathematical Modeling

Background:

• Percutaneous tumor puncture is crucial for diagnosis and treatment but often relies on empirical methods, limiting accuracy and increasing complication risks.
• Current techniques lack quantitative precision, hindering optimal outcomes in tumor interventions.
• Improving percutaneous tumor puncture accuracy and safety remains a significant clinical challenge.

Purpose of the Study:

• To develop and validate a mathematical model for precise, quantitative percutaneous tumor puncture.
• To enhance the accuracy, efficiency, and safety of tumor diagnosis and treatment procedures.
• To reduce patient pain and improve overall clinical outcomes.

Main Methods:

• Mathematical modeling was employed to construct optimal tumor puncture paths and determine needle entry angles.
• Clinical data from CT and ultrasound imaging were extracted to calculate personalized puncture deviation angles and schemes.
• Targeted punctures were performed under B-ultrasound guidance using the developed model.

Main Results:

• The mathematical model significantly increased puncture accuracy by approximately 30% compared to conventional methods.
• Puncture procedures were more efficient, reducing the number of punctures by 50% and operation time by 20%.
• The model enhanced doctor satisfaction by 20% and reduced patient discomfort by 25%.

Conclusions:

• A novel mathematical model integrating imaging data and clinical parameters enables accurate and personalized percutaneous tumor puncture.
• The model demonstrably improves puncture effectiveness, patient safety, and reduces invasiveness.
• This approach shows significant potential for widespread clinical application in tumor interventions.