Comparative Study on Medical Artificial Intelligence Algorithm Assisted and Conventional Imaging Examination Methods

Chest wall tumor is one of the important diseases in thoracic surgery, which can be classified as benign or malignant according to the nature of the tumor. At present, surgery is still the main treatment for this disease, even for a few malignant tumors that are sensitive to radiotherapy and chemotherapy, further surgical treatment is still needed after initial internal medicine treatment. Due to the different nature and location of tumors, the scope of surgical resection may vary greatly. Clinical studies have found that surgery for chest wall tumors requires the adoption of appropriate treatment plans based on the pathological results of the tumor, the location of tumor growth, the degree of local invasion of the tumor, and the presence of metastasis at the time of tumor detection. Further research has found that surgical resection of primary chest wall malignant tumors should be performed under careful planning, as most patients only have one chance of cure, and reoperation after tumor recurrence or surgical failure becomes very difficult. Moreover, even if these patients undergo reoperation, their prognosis is poor.

Malignant chest wall tumors require extensive resection to ensure the thoroughness of the surgery. However, the extensive chest wall defect formed after extensive resection can lead to the destruction of the integrity and stability of the chest wall. If not handled properly, it can cause chest wall softening, abnormal breathing, and acute respiratory failure in the early postoperative period, affecting the therapeutic effect of the surgery; However, in the late stage after surgery, chest wall deformities, pulmonary hernias, chronic respiratory dysfunction, and even scoliosis may occur, which can affect the quality of life.

Therefore, for chest wall tumor surgery, it is not only required to achieve thorough enlargement and resection, but also to consider precise resection to preserve the normal structure of the chest wall as much as possible to avoid adverse consequences. For some well-defined malignant tumors of the chest wall, determining the surgical resection margin is relatively simple. However, for other malignant tumors of the chest wall, such as those with invasive growth, those that recur after the first surgery, or those that recur locally after radiotherapy and chemotherapy, it is often difficult to determine the appropriate surgical resection range and resection margin in clinical practice. At this point, the surgical plan can only be determined and formulated based on the clinical experience of the surgeon and traditional imaging examination results before surgery, which has great uncertainty and increases the complexity and difficulty of the surgery.

Medical artificial intelligence is the in-depth application of artificial intelligence technology in the field of medicine. It integrates knowledge from multiple disciplines such as computer science, data science, and biomedical engineering, aiming to improve the efficiency, accuracy, and personalization of medical services by simulating human intelligent behavior. MedAI provides intelligent services for physicians to assist in diagnosis [5-7], recommend treatment methods, and monitor patients by processing and analyzing massive amounts of medical data, thereby optimizing the allocation of medical resources and improving the patient's medical experience.

At present, MedAI is mainly focused on screening lung nodules, determining the nature of lung nodules, and providing three-dimensional simulation imaging of lung nodules as a reference for surgical methods in the field of thoracic surgery. However, there have been no reports on its application in the clinical treatment of chest wall tumors. Therefore, we plan to conduct research in this area to broaden the application of MedAI in general thoracic surgery and provide better medical quality services for chest wall tumor patients, especially those with malignant chest wall tumors.

In the field of surgical planning for chest wall tumors, conventional imaging methods such as CT and MRI can provide basic anatomical information, but they have limitations. Doctors need to manually analyze two-dimensional images, which makes it difficult to accurately construct the three-dimensional spatial relationship between tumors and complex chest wall structures (such as ribs, blood vessels, and nerves), especially when the tumor boundaries are blurred and adhered to surrounding tissues, which can easily lead to surgical planning deviations and affect the integrity and safety of tumor resection The significant advantages of medical artificial intelligence algorithms in this regard lie in precise tumor localization, multidimensional data analysis, and surgical plan optimization.