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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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Pham TD, Sun X. Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients. Cancer Med 2023; 12:21502-21518. [PMID: 38014709 PMCID: PMC10726782 DOI: 10.1002/cam4.6672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/25/2023] [Accepted: 10/20/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistochemical images, to predict the 5-year survival time of two patient groups: one with preoperative radiotherapy and one without. METHODS The utilization of deep convolutional neural networks in medical research, particularly in clinical cancer studies, has been gaining substantial attention. This success primarily stems from their ability to extract intricate image features that prove invaluable in machine learning. Another innovative method for extracting features at multiple levels is the wavelet-scattering network. Our study combines the strengths of these two convolution-based approaches to robustly extract image features related to protein expression. RESULTS The efficacy of our approach was evaluated across various tissue types, including tumor, biopsy, metastasis, and adjacent normal tissue. Statistical assessments demonstrated exceptional performance across a range of metrics, including prediction accuracy, classification accuracy, precision, and the area under the receiver operating characteristic curve. CONCLUSION These results underscore the potential of dual convolutional learning to assist clinical researchers in the timely validation and discovery of cancer biomarkers.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry Queen MaryUniversity of London Turner StreetLondonUK
| | - Xiao‐Feng Sun
- Division of Oncology Department of Biomedical and Clinical SciencesLinkoping UniversityLinkopingSweden
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Zhang H, Xi Q, Zhang F, Li Q, Jiao Z, Ni X. Application of Deep Learning in Cancer Prognosis Prediction Model. Technol Cancer Res Treat 2023; 22:15330338231199287. [PMID: 37709267 PMCID: PMC10503281 DOI: 10.1177/15330338231199287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023] Open
Abstract
As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.
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Affiliation(s)
- Heng Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
| | - Qianyi Xi
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Fan Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Qixuan Li
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Xinye Ni
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
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