Artificial intelligence-aided diagnostic imaging : A state-of-the-art technique in precancerous screening
© 2023 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd..
BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy.
METHODS: This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent).
RESULTS: The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other.
CONCLUSION: Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
|---|---|
| Enthalten in: |
Journal of gastroenterology and hepatology - 39(2024), 3 vom: 01. März, Seite 544-551 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Lu, Yang-Bor [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 05.03.2024 Date Revised 05.03.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1111/jgh.16429 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM365507830 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM365507830 | ||
| 003 | DE-627 | ||
| 005 | 20240305232103.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/jgh.16429 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1317.xml |
| 035 | |a (DE-627)NLM365507830 | ||
| 035 | |a (NLM)38059883 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lu, Yang-Bor |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence-aided diagnostic imaging |b A state-of-the-art technique in precancerous screening |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 05.03.2024 | ||
| 500 | |a Date Revised 05.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd. | ||
| 520 | |a BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy | ||
| 520 | |a METHODS: This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent) | ||
| 520 | |a RESULTS: The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other | ||
| 520 | |a CONCLUSION: Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Chromoendoscopy | |
| 650 | 4 | |a Image-enhanced endoscopy | |
| 650 | 4 | |a Virtual indigo carmine staining | |
| 650 | 7 | |a Indigo Carmine |2 NLM | |
| 650 | 7 | |a D3741U8K7L |2 NLM | |
| 650 | 7 | |a Carmine |2 NLM | |
| 650 | 7 | |a CID8Z8N95N |2 NLM | |
| 700 | 1 | |a Lu, Si-Cun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Fu-Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Le, Puo-Hsien |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Kai-Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Zi-Zheng |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yung-Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Weng, Yu-Chieh |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Wei-Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Yi-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Jun-Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Chiu, Cheng-Tang |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Qin-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Wei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of gastroenterology and hepatology |d 1989 |g 39(2024), 3 vom: 01. März, Seite 544-551 |w (DE-627)NLM012635235 |x 1440-1746 |7 nnns |
| 773 | 1 | 8 | |g volume:39 |g year:2024 |g number:3 |g day:01 |g month:03 |g pages:544-551 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/jgh.16429 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 39 |j 2024 |e 3 |b 01 |c 03 |h 544-551 | ||