Welcome to Francis Academic Press

Academic Journal of Medicine & Health Sciences, 2023, 4(12); doi: 10.25236/AJMHS.2023.041204.

Research progress of machine learning in the diagnosis and prediction of acute abdomen


Liu Zhichen1, Luo Xu2

Corresponding Author:
Luo Xu

1School of Nursing, Zunyi Medical University, Zunyi, 563000, China

2School of Information Engineering, Zunyi Medical University, Zunyi, 563000, China


Acute abdomen is one of the most common diseases in the emergency department, referring to a group of abdominal diseases that are characterized by acute onset, rapid onset, and frequent changes, with abdominal pain as the main symptom and require urgent treatment. Machine learning is a major research direction in artificial intelligence, with the ability to analyze a large amount of complex data and extract data patterns from a large amount of data, thereby forming rules for data classification and prediction. In recent years, with the informatization of patient record data, research on the combination of machine learning and medical treatment has been increasing. Some studies believe that machine learning algorithms have brought new possibilities for the diagnosis and prediction of acute abdomen. This article reviews the research progress of machine learning in the diagnosis and prediction of acute abdomen, including experimental data, feature selection, algorithm models, and performance evaluation indicators, A systematic summary of the research status of machine learning technology in the application of acute abdominal diseases was conducted. Firstly, with regard to machine learning algorithms, we explicate the utilization of such algorithms in the context of acute abdomen. Secondly, focusing on practical applications, this article elaborates on disease assisted diagnosis and disease prediction through specific experiments; Finally, the limitations of machine learning in the application of acute abdomen and propose prospects were pointed out.


Acute Abdomen, Machine Learning, Artificial Intelligence, Computer-Assisted Diagnosis, Predictions

Cite This Paper

Liu Zhichen, Luo Xu. Research progress of machine learning in the diagnosis and prediction of acute abdomen. Academic Journal of Medicine & Health Sciences (2023) Vol. 4, Issue 12: 27-34. https://doi.org/10.25236/AJMHS.2023.041204.


[1] Laméris W, van Randen A, van Es H W, et al. Imaging strategies for detection of urgent conditions in patients with acute abdominal pain: diagnostic accuracy study[J]. BMJ, 2009, 338:b2431.

[2] Macaluso C R, McNamara R M. Evaluation and management of acute abdominal pain in the emergency department[J]. Int J Gen Med, 2012,5:789-797.

[3] Kucukkartallar T, Cakir M, Tekin A, et al. Estimation of the capacity of emergency surgery in Konya: Nine-year multicenter study[J]. Turkish Journal of Surgery, 2016,32(4):252-255.

[4] Hustey F M, Meldon S W, Banet G A, et al. The use of abdominal computed tomography in older ED patients with acute abdominal pain[J]. Am J Emerg Med, 2005,23(3):259-265.

[5] Lewis L M, Banet G A, Blanda M, et al. Etiology and clinical course of abdominal pain in senior patients: a prospective, multicenter study[J]. J Gerontol A Biol Sci Med Sci, 2005,60(8):1071-1076.

[6] LIU X S, ZHANG X Y. Management principles of pregnancy complicated with surgical acute abdomen[J]. Chinese Journal of Practical Gynecology and Obstetrics, 1999(08):55-56.

[7] YANG Z H, ZHANG C M. Acute abdomen in children [J]. Chinese Journal of Practical Pediatric Clinical Practice,2018,33(06):418-421

[8] GUO R F, LI J L et al. Epidemiological survey of pre hospital emergency patients in Haishi City in 2007[J]. Chinese Journal of Emergency Medicine,2008(11):1127-1130.

[9] Stoker J, van Randen A, Laméris W, et al. Imaging patients with acute abdominal pain[J]. Radiology, 2009,253(1):31-46.

[10] Marincek B. Nontraumatic abdominal emergencies: acute abdominal pain: diagnostic strategies [J]. Eur Radiol, 2002,12(9):2136-2150.

[11] Gutierrez M, Veronez C L, Rodrigues V S, et al. Unnecessary Abdominal Surgeries in Attacks of Hereditary Angioedema with Normal C1 Inhibitor[J]. Clin Rev Allergy Immunol, 2021,61(1):60-65.

[12] ZHU C F, HOU X K, TAO Y et al. Clinical application and misdiagnosis reasons of emergency ultrasound diagnosis of elderly acute abdomen [J]. Chinese Journal of Gerontology, 2016,36 (12): 3011-3012

[13] LIU H Q, HAO M, WANG Y H et al. A 10-year retrospective summary and misdiagnosis analysis of patients with acute abdomen during pregnancy [J].China Health Statistics, 2014,31 (03): 515-517

[14] GU Z, LU X D, LU Q S et al. Analysis of 44 cases of misdiagnosis of acute appendicitis in gynecological acute abdomen [J] Chinese Journal of Practical Surgery, 2009,29 (S1): 137-138

[15] ZHENG J Z. Clinical analysis of 63 cases of atypical acute abdomen misdiagnosed as acute appendicitis [J] Shandong Pharmaceutical, 2008 (28): 98-99

[16] Hamet P, Tremblay J. Artificial intelligence in medicine[J]. Metabolism, 2017,69:S36-S40.

[17] Tang K J W, Ang C K E, Constantinides T, et al. Artificial Intelligence and Machine Learning in Emergency Medicine[J]. Biocybernetics and Biomedical Engineering, 2021,41(1):156-172.

[18] ZHAO X Q, LIU Y, PENG R et al. The clinical application of artificial intelligence in imaging examination of acute abdomen [J] China Digital Medicine, 2020,15 (11): 33-35 

[19] Cornet G. Robot companions and ethics a pragmatic approach of ethical design[J]. J Int Bioethique, 2013,24(4):49-58, 179-180.

[20] Deo R C. Machine Learning in Medicine[J]. Circulation, 2015,132(20):1920-1930.

[21] ZHOU Z H.Machine Learning [M] Beijing: Tsinghua University Press, 2016 

[22] Shepherd J A. Computer-aided diagnosis of acute abdominal pain[J]. Br Med J, 1972, 2(5809): 347-348.

[23] Khumrin P, Ryan A, Judd T, et al. Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students[J]. Stud Health Technol Inform, 2017, 245: 447-451.

[24] McAdam W A, Brock B M, Armitage T, et al. Twelve years' experience of computer-aided diagnosis in a district general hospital[J]. Ann R Coll Surg Engl, 1990,72(2):140-146.

[25] de Dombal F T, Matharu S S, Staniland J R, et al. Presentation of cancer to hospital as 'acute abdominal pain'[J]. Br J Surg, 1980,67(6):413-416.

[26] Wilson D H, Wilson P D, Walmsley R G, et al. Diagnosis of acute abdominal pain in the accident and emergency department[J]. Br J Surg, 1977,64(4):250-254.

[27] Papadopoulos H G A V V. Reliable diagnosis of acute abdominal pain with conformal prediction[J]. Engineering Intelligent Systems, 2009,17(2):127.

[28] LANG J H. Gynecological acute abdomen [J] Chinese Journal of Obstetrics and Gynecology, 2022,57 (03): 161-163

[29] Walmsley G L, Wilson D H, Gunn A A, et al. Computer-aided diagnosis of lower abdominal pain in women[J]. Br J Surg, 1977,64(8):538-541.

[30] Wilk S, Słowiński R, Michałowski W, et al. Supporting triage of children with abdominal pain in the emergency room[J]. European Journal of Operational Research, 2005,160(3):696-709.

[31] Tenório J M, Hummel A D, Cohrs F M, et al. Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease[J]. International Journal of Medical Informatics, 2011,80(11):793-802.

[32] Reismann J, Romualdi A, Kiss N, et al. Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach[J]. PLoS One, 2019,14(9):e222030.

[33] Park J J, Kim K A, Nam Y, et al. Convolutional-neural-network-based diagnosis of appendicitis via CT scans in patients with acute abdominal pain presenting in the emergency department[J]. Sci Rep, 2020,10(1):9556.

[34] Mashayekhi R, Parekh V S, Faghih M, et al. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis[J]. Eur J Radiol, 2020,123:108778.

[35] Si K, Xue Y, Yu X, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors[J]. Theranostics, 2021,11(4):1982-1990.

[36] Marya N B, Powers P D, Chari S T, et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis[J]. Gut, 2021,70(7):1335-1344.

[37] Matsui Y, Hirooka S, Kotsuka M, et al. Prognosis in Patients With Gallbladder Edema Misdiagnosed as Cholecystitis[J]. JSLS, 2019,23(2).

[38] Yu C J, Yeh H J, Chang C C, et al. Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound[J]. Comput Methods Programs Biomed, 2021, 211: 106382.

[39] Qu C, Gao L, Yu X Q, et al. Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients[J]. Gastroenterol Res Pract, 2020,2020:3431290.

[40] Fei Y, Gao K, Li W Q. Prediction and evaluation of the severity of acute respiratory distress syndrome following severe acute pancreatitis using an artificial neural network algorithm model[J]. HPB (Oxford), 2019,21(7):891-897.

[41] Fei Y, Hu J, Li W Q, et al. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis[J]. J Thromb Haemost, 2017, 15(3):439-445.

[42] Sun H W, Lu J Y, Weng Y X, et al. Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements[J]. Aging (Albany NY), 2021,13(6):8817-8834.

[43] Stathopoulos G P, Androulakis N, Souglakos J, et al. Present treatment and future expectations in advanced pancreatic cancer[J]. Anticancer Res, 2008,28(2B):1303-1308.

[44] Carreras-Torres R, Johansson M, Gaborieau V, et al. The Role of Obesity, Type 2 Diabetes, and Metabolic Factors in Pancreatic Cancer: A Mendelian Randomization Study[J]. J Natl Cancer Inst, 2017,109(9).

[45] Hsieh M H, Sun L M, Lin C L, et al. Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models[J]. Cancer Manag Res, 2018,10:6317-6324.

[46] Hsu C C, Chu C J, Lin C H, et al. A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain[J]. Diagnostics (Basel), 2021,12(1).

[47] Peng J H, Fang Y J, Li C X, et al. A scoring system based on artificial neural network for predicting 10-year survival in stage II A colon cancer patients after radical surgery[J]. Oncotarget, 2016,7(16):22939-22947.