Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review
Chun-Liang Lai, Riya Karmakar, Arvind Mukundan, Ragul Kumar Natarajan, Song-Cun Lu, Cheng-Yi Wang, Hsiang-Chen Wang · 2024
Read Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review by Chun-Liang Lai, Riya Karmakar, Arvind Mukundan, Ragul Kumar Natarajan, Song-Cun Lu, Cheng-Yi Wang, Hsiang-Chen Wang (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11629177.txt.
Description
Chun-Liang Lai and co-authors review hyperspectral imaging for tissue diagnosis, in a paper published in 2024. The method records many narrow bands of light from tissue, far more than a normal camera, revealing chemistry that eyes cannot see. Paired with machine learning, it promises early, precise diagnosis without cutting tissue.
The review covers uses from diabetic eye disease and brain disorders to tooth decay and skin cancer. It then weighs what blocks clinic use: complex gear, the need for expert training, and uneven workflows. Companies already sell advanced systems, a sign of strong commercial interest. The authors argue that simpler tools, shared standards, and staff training can move the method into real-time care. The prize is faster answers at the bedside.
Catalog record
| CREATOR | Chun-Liang Lai, Riya Karmakar, Arvind Mukundan, Ragul Kumar Natarajan, Song-Cun Lu, Cheng-Yi Wang, Hsiang-Chen Wang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |