Cited 0 times in
Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, KW | - |
dc.contributor.author | Huh, J | - |
dc.contributor.author | Urooj, B | - |
dc.contributor.author | Lee, J | - |
dc.contributor.author | Lee, J | - |
dc.contributor.author | Lee, IS | - |
dc.contributor.author | Park, H | - |
dc.contributor.author | Na, S | - |
dc.contributor.author | Ko, Y | - |
dc.date.accessioned | 2023-09-11T06:01:46Z | - |
dc.date.available | 2023-09-11T06:01:46Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2093-582X | - |
dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/26345 | - |
dc.description.abstract | Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer. | - |
dc.language.iso | en | - |
dc.title | Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry | - |
dc.type | Article | - |
dc.identifier.pmid | 37553127 | - |
dc.identifier.url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412978 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Diagnostic imaging | - |
dc.subject.keyword | Gastric cancer | - |
dc.subject.keyword | Sarcopenia | - |
dc.contributor.affiliatedAuthor | Huh, J | - |
dc.type.local | Journal Papers | - |
dc.identifier.doi | 10.5230/jgc.2023.23.e30 | - |
dc.citation.title | Journal of gastric cancer | - |
dc.citation.volume | 23 | - |
dc.citation.number | 3 | - |
dc.citation.date | 2023 | - |
dc.citation.startPage | 388 | - |
dc.citation.endPage | 399 | - |
dc.identifier.bibliographicCitation | Journal of gastric cancer, 23(3). : 388-399, 2023 | - |
dc.identifier.eissn | 2093-5641 | - |
dc.relation.journalid | J02093582X | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.