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©The Author(s) 2021.
World J Gastroenterol. Apr 7, 2021; 27(13): 1283-1295
Published online Apr 7, 2021. doi: 10.3748/wjg.v27.i13.1283
Published online Apr 7, 2021. doi: 10.3748/wjg.v27.i13.1283
Ref. | Study design | Data source | AI instrument | Patient | Aim | Accuracy |
Norton et al[14], 2001 | Retrospective | Standard EUS | ANN | 21 | PDAC vs CP | 89% |
Ozkan et al[15], 2015 | Retrospective | Standard EUS | ANN | 332 | PDAC vs Nl | 89%-92% |
Zhang et al[16], 2010 | Retrospective | Standard EUS | ANN | 216 | PDAC vs Nl | 98% |
Das et al[17], 2008 | Retrospective | Standard EUS | ANN | 56 | PDAC vs Nl vs CP | 93% |
Zhu et al[18], 2013 | Retrospcective | Standard EUS | ANN | 388 | PDAC vs CP | 94% |
Săftoiu et al[20], 2012 | Prospective | EUS w/ elastography | ANN | 258 | PDAC vs CP | 91% |
Săftoiu et al[21], 2008 | Prospective | EUS w/elastography | ANN | 68 | PDAC vs CP | 90% |
Săftoiu et al[22], 2015 | Prospective | EUS w/contrast | ANN | 167 | PDAC vs CP | 95%1 |
Fu et al[24], 2018 | Retrospective | CT | ANN | 59 | Pancreatic tumor segmentation | 76%1 |
Chu et al[25], 2019 | Retrospective | CT | Computer derived forest algorithm | 380 | PDAC vs Nl | 99% |
Liu et al[26], 2019 | Retrospective | CT | ANN | 338 | PDAC vs Nl | 76% |
Chu et al[29], 2019 | Retrospective | CT | ANN | 456 | Segmentation of PDAC vs Nl | 94% |
Devi et al[32], 2019 | Retrospective | MRI | ANN | 168 | Nl vs Abnormal pancreas | 96% |
Gao et al[33], 2020 | Retrospective | MRI | ANN | 504 | Identify pancreatic disease | 77% |
Liang et al[34], 2020 | Retrospective | MRI | ANN | 27 | Segmentation of panc tumors | Not explicitly stated |
Muhammad et al[42], 2019 | Retrospective | Clinical variables | ANN | 800114 | PDAC prediction | 85% |
Klein et al[43], 2013 | Retrospective | Clinical variables | Computer derived model | 7003 | PDAC risk | 61% |
Hsieh et al[45], 2018 | Retrospective | Clinical variables | ANN | > 1 million | NOD predicting PDAC | 72% |
Zhao et al[46], 2011 | Retrospective | Clinival variables + Pubmed data | Bayesian network inference | N/A | PDAC prediction | 85% |
Sanoob et al[47], 2016 | Retrospective | Clinical variables | ANN | 120 | PDAC detection | Not explicitly stated |
Momeni-Boroujeni et al[56], 2017 | Retrospective | FNA samples | ANN | 75 | PDAC diagnosis | 77% |
Bhasin et al[58], 2016 | Retrospective | PDAC genes | Computer vector model | 52 | PDAC detection | 92% |
Almeida et al[59], 2020 | Retrospective | PDAC genes | ANN | 402 | PDAC detection | 86% |
- Citation: Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising. World J Gastroenterol 2021; 27(13): 1283-1295
- URL: https://www.wjgnet.com/1007-9327/full/v27/i13/1283.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i13.1283