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This project focuses on the development of a precision multi-organ surgical planning model aided by artificial intelligence (AI), computer-aided design (CAD), multi-material 3D printing, and multimodal 3D modeling. The integration of these advanced technologies aims to enhance the accuracy and efficiency of preoperative planning for complex surgical procedures involving multiple organs. By leveraging AI and CAD, the project seeks to create detailed, patient-specific 3D models that incorporate various imaging modalities. These models will be used to guide multi-material 3D printing, producing highly accurate replicas of anatomical structures. The ultimate goal is to improve surgical outcomes by providing surgeons with precise and comprehensive tools for planning and executing intricate surgeries.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 3D reconstruction | Patients in this group underwent surgical planning with the aid of 3D reconstruction technology, which included preoperative imaging and 3D modeling to assist in the planning of thoracic surgery. |
| |
| Non-3D reconstruction | Patients in this group underwent traditional surgical planning without the use of 3D reconstruction technology, relying on standard preoperative imaging and planning techniques. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 3D Reconstruction Technology | Device | Utilization of 3D reconstruction technology to assist in preoperative planning and surgical procedures for thoracic surgery patients. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Operation Time | Measure the total duration of the surgery in minutes. | During the surgery |
| Blood Loss | Assess the total blood loss during surgery in milliliters. | During the surgery |
| Postoperative Complications | Record any complications within 30 days post-surgery. | Within 30 days post-surgery |
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Inclusion Criteria:
Exclusion Criteria:
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The study includes all lung cancer patients who underwent surgery at the Affiliated Hospital of Hebei University from 2020 to 2023.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Affiliated Hospital of Hebei University | Baoding | Hebei | 071000 | China |
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| No 3D reconstruction is used | Device | This group did not use 3D reconstruction for surgical navigation |
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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