Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review
Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerati...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2024-12-01
|
| Series: | APL Bioengineering |
| Online Access: | http://dx.doi.org/10.1063/5.0240444 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846093518869626880 |
|---|---|
| author | Chun-Liang Lai Riya Karmakar Arvind Mukundan Ragul Kumar Natarajan Song-Cun Lu Cheng-Yi Wang Hsiang-Chen Wang |
| author_facet | Chun-Liang Lai Riya Karmakar Arvind Mukundan Ragul Kumar Natarajan Song-Cun Lu Cheng-Yi Wang Hsiang-Chen Wang |
| author_sort | Chun-Liang Lai |
| collection | DOAJ |
| description | Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption. |
| format | Article |
| id | doaj-art-87d73ed2ad2d4e758c23b3bc528e5d6f |
| institution | Kabale University |
| issn | 2473-2877 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Bioengineering |
| spelling | doaj-art-87d73ed2ad2d4e758c23b3bc528e5d6f2025-01-02T17:08:49ZengAIP Publishing LLCAPL Bioengineering2473-28772024-12-0184041504041504-1810.1063/5.0240444Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive reviewChun-Liang Lai0Riya Karmakar1Arvind Mukundan2Ragul Kumar Natarajan3Song-Cun Lu4Cheng-Yi Wang5Hsiang-Chen Wang6 Division of Pulmonology and Critical Care, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi 62247, Taiwan Department of Mechanical Engineering, National Chung Cheng University, 168, University Road, Min Hsiung, Chiayi City 62102, Taiwan Department of Mechanical Engineering, National Chung Cheng University, 168, University Road, Min Hsiung, Chiayi City 62102, Taiwan Department of Biotechnology, Karpagam Academy of Higher Education, Salem - Kochi Hwy, Eachanari, Coimbatore, Tamil Nadu 641021, India Department of Mechanical Engineering, National Chung Cheng University, 168, University Road, Min Hsiung, Chiayi City 62102, Taiwan Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Kaohsiung City 80284, Taiwan Department of Mechanical Engineering, National Chung Cheng University, 168, University Road, Min Hsiung, Chiayi City 62102, TaiwanHyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.http://dx.doi.org/10.1063/5.0240444 |
| spellingShingle | Chun-Liang Lai Riya Karmakar Arvind Mukundan Ragul Kumar Natarajan Song-Cun Lu Cheng-Yi Wang Hsiang-Chen Wang Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review APL Bioengineering |
| title | Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review |
| title_full | Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review |
| title_fullStr | Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review |
| title_full_unstemmed | Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review |
| title_short | Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review |
| title_sort | advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics a comprehensive review |
| url | http://dx.doi.org/10.1063/5.0240444 |
| work_keys_str_mv | AT chunlianglai advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT riyakarmakar advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT arvindmukundan advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT ragulkumarnatarajan advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT songcunlu advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT chengyiwang advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview AT hsiangchenwang advancinghyperspectralimagingandmachinelearningtoolstowardclinicaladoptionintissuediagnosticsacomprehensivereview |