DIKOApp: An AI-Based Diagnostic System for Knee Osteoarthritis.
The diagnosis of knee osteoarthritis is challenging due to its complex nature and various contributing factors. With the advancement of artificial intelligence (AI) technology, some computer vision-based methods have been developed to address this task. However, when applied in practice, these metho...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 5; pp. 3182 - 3198 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188953416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188953416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Oct2025 vid: 38 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188953416 188953416 189894375 188953416 10.1007/s10278-024-01383-5 188953416 ppf: 3182 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DIKOApp: An AI-Based Diagnostic System for Knee Osteoarthritis. aug: au: Phan, Trung Hieu Nguyen, Trung Tuan Nguyen, Thanh Dat Pham, Huu Hung Ta, Gia Khang Tran, Minh Triet Quan, Thanh Tho affil: https://ror.org/04qva2324 Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam sug: subj: Artificial Intelligence World Wide Web Applications Evaluation Osteoarthritis, Knee Diagnosis Diagnosis, Computer Assisted Methods Image Processing, Computer Assisted Conceptual Framework Human Vietnam Validation Studies Paired T-Tests Health Knowledge Validity Time Factors ab: The diagnosis of knee osteoarthritis is challenging due to its complex nature and various contributing factors. With the advancement of artificial intelligence (AI) technology, some computer vision-based methods have been developed to address this task. However, when applied in practice, these methods encounter numerous challenges. Training a powerful AI model to effectively analyze a wide range of medical images is crucial. On the other hand, collecting and accurately labeling a significant number of medical images in the real world is necessary. Specifically, when dealing with knee images from specific regions like Vietnam, certain unique biological characteristics make it difficult to utilize and trust previously published studies. To effectively address these challenges, we introduce DIKOApp, an automatic diagnostic application for knee osteoarthritis based on the DIKO framework, trained on a dataset specifically built for the Vietnamese population. This framework is designed with two stages that leverage medical knowledge and computer vision techniques. The DIKO framework leverages efficient data sampling and augmentation framework to handle medical images in the real world more effectively. When evaluated using a real-world knee image dataset from Vietnamese individuals, the DIKO model demonstrates impressive performance with an accuracy of 89.34% and an F1-score of 0.88. By utilizing the capabilities of the DIKO framework, DIKOApp shows practical and promising real-world potential, enabling doctors and healthcare service providers to diagnose pathological conditions more accurately while requiring less diagnostic time, thereby improving the lives of patients. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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