AI-powered Innovations in Pancreatitis Imaging: a Comprehensive Literature Synthesis

Overview

Journal Abdom Radiol (NY)

Publisher Springer

Specialties Gastroenterology
Radiology

Date 2024 Aug 12

PMID 39133362

Authors

Sebastian Maletz

Yoga Balagurunathan

Kade Murphy

Les Folio

Ranjit Chima

Atif Zaheer

Harshna Vadvala

Affiliations

Soon will be listed here.

Abstract

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.

References

Mortele K, Wiesner W, Intriere L, Shankar S, Zou K, Kalantari B . A modified CT severity index for evaluating acute pancreatitis: improved correlation with patient outcome. AJR Am J Roentgenol. 2004; 183(5):1261-5. DOI: 10.2214/ajr.183.5.1831261. View

Chartrand G, Cheng P, Vorontsov E, Drozdzal M, Turcotte S, Pal C . Deep Learning: A Primer for Radiologists. Radiographics. 2017; 37(7):2113-2131. DOI: 10.1148/rg.2017170077. View

Zhang Z, Sejdic E . Radiological images and machine learning: Trends, perspectives, and prospects. Comput Biol Med. 2019; 108:354-370. PMC: 6531364. DOI: 10.1016/j.compbiomed.2019.02.017. View

Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y . A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020; 101(12):765-770. DOI: 10.1016/j.diii.2020.10.001. View

Song Y, Lu Y . Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry. 2015; 27(2):130-5. PMC: 4466856. DOI: 10.11919/j.issn.1002-0829.215044. View

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout R, Granton P . Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012; 48(4):441-6. PMC: 4533986. DOI: 10.1016/j.ejca.2011.11.036. View

Shen D, Wu G, Suk H . Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017; 19:221-248. PMC: 5479722. DOI: 10.1146/annurev-bioeng-071516-044442. View

Gao X, Wang X . Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study. Diagn Interv Imaging. 2019; 101(2):91-100. DOI: 10.1016/j.diii.2019.07.002. View

Kumar V, Gu Y, Basu S, Berglund A, Eschrich S, Schabath M . Radiomics: the process and the challenges. Magn Reson Imaging. 2012; 30(9):1234-48. PMC: 3563280. DOI: 10.1016/j.mri.2012.06.010. View

10.

Kocak B, Durmaz E, Ates E, Kilickesmez O . Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. 2019; 25(6):485-495. PMC: 6837295. DOI: 10.5152/dir.2019.19321. View

11.

Chan H, Samala R, Hadjiiski L, Zhou C . Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020; 1213:3-21. PMC: 7442218. DOI: 10.1007/978-3-030-33128-3_1. View

12.

Folio L, Turkbey E, Steinberg S, Apolo A . Viable tumor volume: Volume of interest within segmented metastatic lesions, a pilot study of proposed computed tomography response criteria for urothelial cancer. Eur J Radiol. 2015; 84(9):1708-14. PMC: 4545379. DOI: 10.1016/j.ejrad.2015.05.026. View

13.

Johnson C, Stephens D, Sarr M . CT of acute pancreatitis: correlation between lack of contrast enhancement and pancreatic necrosis. AJR Am J Roentgenol. 1991; 156(1):93-5. DOI: 10.2214/ajr.156.1.1898576. View

14.

Chu L, Park S, Kawamoto S, Fouladi D, Shayesteh S, Zinreich E . Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. AJR Am J Roentgenol. 2019; 213(2):349-357. DOI: 10.2214/AJR.18.20901. View

15.

Lofstedt T, Brynolfsson P, Asklund T, Nyholm T, Garpebring A . Gray-level invariant Haralick texture features. PLoS One. 2019; 14(2):e0212110. PMC: 6386443. DOI: 10.1371/journal.pone.0212110. View

16.

Mashayekhi R, Parekh V, Faghih M, Singh V, Jacobs M, Zaheer A . Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol. 2019; 123:108778. PMC: 7968044. DOI: 10.1016/j.ejrad.2019.108778. View

17.

Parekh V, Jacobs M . Deep learning and radiomics in precision medicine. Expert Rev Precis Med Drug Dev. 2019; 4(2):59-72. PMC: 6508888. DOI: 10.1080/23808993.2019.1585805. View

18.

Keogan M, Lo J, Freed K, Raptopoulos V, Blake S, Kamel I . Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol. 2002; 9(4):410-9. DOI: 10.1016/s1076-6332(03)80186-1. View

19.

Choi H, Park H, Choi S, Do J, Yoon N, Ko A . Early Prediction of the Severity of Acute Pancreatitis Using Radiologic and Clinical Scoring Systems With Classification Tree Analysis. AJR Am J Roentgenol. 2018; 211(5):1035-1043. DOI: 10.2214/AJR.18.19545. View

20.

Ince A, Silahtaroglu G, Seven G, Kochan K, Yildiz K, Senturk H . Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence. Pancreatology. 2023; 23(2):176-186. DOI: 10.1016/j.pan.2022.12.005. View