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- Publisher Website: 10.1097/JS9.0000000000000979
- Scopus: eid_2-s2.0-85187958774
- PMID: 38051932
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Article: Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders
Title | Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders |
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Authors | |
Issue Date | 1-Mar-2024 |
Publisher | Wolters Kluwer Health |
Citation | International journal of surgery, 2024, v. 110, n. 3, p. 1677-1686 How to Cite? |
Abstract | Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice. |
Persistent Identifier | http://hdl.handle.net/10722/348267 |
ISSN | 2023 Impact Factor: 12.5 2023 SCImago Journal Rankings: 2.895 |
DC Field | Value | Language |
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dc.contributor.author | Adeoye, John | - |
dc.contributor.author | Su, Yu Xiong | - |
dc.date.accessioned | 2024-10-08T00:31:19Z | - |
dc.date.available | 2024-10-08T00:31:19Z | - |
dc.date.issued | 2024-03-01 | - |
dc.identifier.citation | International journal of surgery, 2024, v. 110, n. 3, p. 1677-1686 | - |
dc.identifier.issn | 1743-9191 | - |
dc.identifier.uri | http://hdl.handle.net/10722/348267 | - |
dc.description.abstract | <p>Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.</p> | - |
dc.language | eng | - |
dc.publisher | Wolters Kluwer Health | - |
dc.relation.ispartof | International journal of surgery | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders | - |
dc.type | Article | - |
dc.identifier.doi | 10.1097/JS9.0000000000000979 | - |
dc.identifier.pmid | 38051932 | - |
dc.identifier.scopus | eid_2-s2.0-85187958774 | - |
dc.identifier.volume | 110 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 1677 | - |
dc.identifier.epage | 1686 | - |
dc.identifier.eissn | 1743-9159 | - |
dc.identifier.issnl | 1743-9159 | - |