Ablation assessment method, ablation assessment system, and storage medium
By identifying the lesion and ablation area from images immediately after ablation, and combining this with a recurrence prediction model to assess the ablation effect and recurrence risk, the problem of inaccurate traditional ablation assessment is solved, achieving immediate assessment and efficient recurrence prediction.
CN122156036APending Publication Date: 2026-06-05WUHAN UNITED IMAGING HEALTHCARE SURGICAL TECH CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNITED IMAGING HEALTHCARE SURGICAL TECH CO LTD
- Filing Date
- 2024-12-05
- Publication Date
- 2026-06-05
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Figure CN122156036A_ABST
Abstract
The application relates to an ablation evaluation method, an ablation evaluation system and a storage medium. The method comprises the following steps: determining a lesion area from a preoperative image of a target object, and determining an ablation area from a postoperative ablation image of the target object; performing difference analysis according to the lesion area and the ablation area to determine an ablation rate; the ablation rate represents the ablation degree of the lesion; inputting related information of the lesion corresponding to the lesion area and the ablation rate into a preset recurrence prediction model to perform evaluation, and obtaining an ablation evaluation result; the ablation evaluation result comprises recurrence information and / or supplementary ablation reference information determined based on the recurrence information. In one aspect, the ablation effect is evaluated based on the lesion area and the ablation area obtained immediately after the operation, and supplementary ablation is performed in time, so that the ablation effect is improved, and the number of ablation times is reduced. In another aspect, the related information of the lesion and the ablation rate are evaluated and analyzed based on a recurrence prediction model, so that more accurate evaluation results can be obtained, and the accuracy of recurrence prediction is improved.
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