Chest wall tumors are one of the important diseases in thoracic surgery, and surgery remains the main method for treating this disease in clinical practice. The surgery for chest wall tumors requires extensive resection, and more importantly, precise resection. If the resection range is insufficient, it is easy to cause tumor recurrence and metastasis, which affects the patient's survival; If the resection range is too large, it will cause damage to the chest wall structure, affecting the patient's postoperative recovery and quality of life. At present, the determination of the surgical resection range mainly relies on the experience of the surgeon and the results of imaging examinations. Even if experienced surgeons still have multiple imaging examination results, there are still clinical difficulties of insufficient or excessive resection. Medical artificial intelligence is the in-depth application of artificial intelligence technology in the field of medicine. By processing and analyzing massive amounts of medical data, it can accurately locate tumors and optimize surgical plans. Therefore, it is proposed to compare the clinical effects of surgical resection of chest wall tumors using medical artificial intelligence algorithms and conventional imaging examination methods, in order to understand whether it can achieve more accurate tumor resection.
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