Deep learning in oral cancer- a systematic review
Abstract Background Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. Methods This systematic review was...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-024-03993-5 |
| _version_ | 1797272963052994560 |
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| author | Kritsasith Warin Siriwan Suebnukarn |
| author_facet | Kritsasith Warin Siriwan Suebnukarn |
| author_sort | Kritsasith Warin |
| collection | DOAJ |
| description | Abstract Background Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. Methods This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. Results Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0–100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78–0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77–4687.39) for classification studies. Conclusions The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer. |
| first_indexed | 2024-03-07T14:37:47Z |
| format | Article |
| id | doaj.art-66009241a61f428f934b84c78773aafd |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-03-07T14:37:47Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-66009241a61f428f934b84c78773aafd2024-03-05T20:33:42ZengBMCBMC Oral Health1472-68312024-02-0124112110.1186/s12903-024-03993-5Deep learning in oral cancer- a systematic reviewKritsasith Warin0Siriwan Suebnukarn1Faculty of Dentistry, Thammasat UniversityFaculty of Dentistry, Thammasat UniversityAbstract Background Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. Methods This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. Results Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0–100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78–0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77–4687.39) for classification studies. Conclusions The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.https://doi.org/10.1186/s12903-024-03993-5Artificial intelligenceDeep learningNeural networkOral cancerSystematic review |
| spellingShingle | Kritsasith Warin Siriwan Suebnukarn Deep learning in oral cancer- a systematic review BMC Oral Health Artificial intelligence Deep learning Neural network Oral cancer Systematic review |
| title | Deep learning in oral cancer- a systematic review |
| title_full | Deep learning in oral cancer- a systematic review |
| title_fullStr | Deep learning in oral cancer- a systematic review |
| title_full_unstemmed | Deep learning in oral cancer- a systematic review |
| title_short | Deep learning in oral cancer- a systematic review |
| title_sort | deep learning in oral cancer a systematic review |
| topic | Artificial intelligence Deep learning Neural network Oral cancer Systematic review |
| url | https://doi.org/10.1186/s12903-024-03993-5 |
| work_keys_str_mv | AT kritsasithwarin deeplearninginoralcancerasystematicreview AT siriwansuebnukarn deeplearninginoralcancerasystematicreview |