Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Updates in Surgery

Open Access 12-03-2024 | Appendicitis | Review Article

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Authors: Valentina Bianchi, Mauro Giambusso, Alessandra De Iacob, Maria Michela Chiarello, Giuseppe Brisinda

Published in: Updates in Surgery

Login to get access

Abstract

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons’ ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.
Literature
1.
2.
Stajic J, Stone R, Chin G, Wible B (2015) Artificial intelligence. Rise of the machines. Science 349(6245):248–249PubMedCrossRefADS
3.
4.
5.
Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G (2024) Navigating the doctor-patient-AI relationship—a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC Prim Care 25(1):42PubMedPubMedCentralCrossRef
6.
Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268(1):70–76PubMedCrossRef
7.
Kaul V, Enslin S, Gross SA (2020) History of artificial intelligence in medicine. Gastrointest Endosc 92(4):807–812PubMedCrossRef
8.
Loftus TJ, Altieri MS, Balch JA, Abbott KL, Choi J, Marwaha JS, Hashimoto DA, Brat GA, Raftopoulos Y, Evans HL et al (2023) Artificial intelligence-enabled decision support in surgery: state-of-the-art and future directions. Ann Surg 278(1):51–58PubMedCrossRef
9.
10.
11.
Dasgupta P (2019) Artificial intelligence, three-dimensional printing and global health. BJU Int 124(6):897PubMedCrossRef
12.
Park T, Gu P, Kim CH, Kim KT, Chung KJ, Kim TB, Jung H, Yoon SJ, Oh JK (2023) Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea. Prostate Int 11(4):218–221PubMedPubMedCentralCrossRef
13.
Martinez-Romero M, Vazquez-Naya JM, Rabunal JR, Pita-Fernandez S, Macenlle R, Castro-Alvarino J, Lopez-Roses L, Ulla JL, Martinez-Calvo AV, Vazquez S et al (2010) Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr Drug Metab 11(4):347–368PubMedCrossRef
14.
Rao HB, Sastry NB, Venu RP, Pattanayak P (2022) The role of artificial intelligence based systems for cost optimization in colorectal cancer prevention programs. Front Artif Intell 5:955399PubMedPubMedCentralCrossRef
15.
Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK (2023) Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. Explor Target Antitumor Ther 4(6):1286–1300PubMedPubMedCentralCrossRef
16.
Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y (2020) Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 146(9):2339–2350PubMedCrossRef
17.
Kuwayama N, Hoshino I, Mori Y, Yokota H, Iwatate Y, Uno T (2023) Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancer. Oncol Lett 26(5):499PubMedPubMedCentralCrossRef
18.
Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA (2021) Artificial Intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research. World J Hepatol 13(12):1977–1990PubMedPubMedCentralCrossRef
19.
Han IW, Cho K, Ryu Y, Shin SH, Heo JS, Choi DW, Chung MJ, Kwon OC, Cho BH (2020) Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence. World J Gastroenterol 26(30):4453–4464PubMedPubMedCentralCrossRef
20.
Machry M, Ferreira LF, Lucchese AM, Kalil AN, Feier FH (2023) Liver volumetric and anatomic assessment in living donor liver transplantation: the role of modern imaging and artificial intelligence. World J Transplant 13(6):290–298PubMedPubMedCentralCrossRef
21.
Yu YD, Lee KS, Man Kim J, Ryu JH, Lee JG, Lee KW, Kim BW, Kim DS (2022) Korean Organ Transplantation Registry Study G: Artificial intelligence for predicting survival following deceased donor liver transplantation: retrospective multi-center study. Int J Surg 105:106838PubMedCrossRef
22.
Clarke JR, Cebula DP, Webber BL (1988) Artificial intelligence: a computerized decision aid for trauma. J Trauma 28(8):1250–1254PubMedCrossRef
23.
Kim D, You S, So S, Lee J, Yook S, Jang DP, Kim IY, Park E, Cho K, Cha WC et al (2018) A data-driven artificial intelligence model for remote triage in the prehospital environment. PLoS ONE 13(10):e0206006PubMedPubMedCentralCrossRef
24.
Stonko DP, Guillamondegui OD, Fischer PE, Dennis BM (2021) Artificial intelligence in trauma systems. Surgery 169(6):1295–1299PubMedCrossRef
25.
Litvin A, Korenev S, Rumovskaya S, Sartelli M, Baiocchi G, Biffl WL, Coccolini F, Di Saverio S, Kelly MD, Kluger Y et al (2021) WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg 16(1):50PubMedPubMedCentralCrossRef
26.
Cobianchi L, Piccolo D, Dal Mas F, Agnoletti V, Ansaloni L, Balch J, Biffl W, Butturini G, Catena F, Coccolini F et al (2023) Surgeons’ perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey. World J Emerg Surg 18(1):1PubMedPubMedCentralCrossRef
27.
De Simone B, Abu-Zidan FM, Gumbs AA, Chouillard E, Di Saverio S, Sartelli M, Coccolini F, Ansaloni L, Collins T, Kluger Y et al (2022) Knowledge, attitude, and practice of artificial intelligence in emergency and trauma surgery, the ARIES project: an international web-based survey. World J Emerg Surg 17(1):10PubMedPubMedCentralCrossRef
28.
De Simone B, Chouillard E, Gumbs AA, Loftus TJ, Kaafarani H, Catena F (2022) Artificial intelligence in surgery: the emergency surgeon’s perspective (the ARIES project). Discov Health Syst 1(1):9PubMedPubMedCentralCrossRef
29.
30.
31.
32.
Mohanty S, Harun Ai Rashid M, Mridul M, Mohanty C, Swayamsiddha S (2020) Application of artificial intelligence in COVID-19 drug repurposing. Diabetes Metab Syndr 14(5):1027–1031
33.
34.
Podgorelec V, Kokol P, Stiglic B, Rozman I (2002) Decision trees: an overview and their use in medicine. J Med Syst 26(5):445–463PubMedCrossRef
35.
Hashimoto DA, Ward TM, Meireles OR (2020) The role of artificial intelligence in surgery. Adv Surg 54:89–101PubMedCrossRef
36.
Wang F, Zhang Z, Wu K, Jian D, Chen Q, Zhang C, Dong Y, He X, Dong L (2023) Artificial intelligence techniques for ground fault line selection in power systems: state-of-the-art and research challenges. Math Biosci Eng 20(8):14518–14549PubMedCrossRef
37.
Howell MD, Corrado GS, DeSalvo KB (2024) Three epochs of artificial intelligence in health care. JAMA 331(3):242–244PubMedCrossRef
38.
Zou J, Han Y, So SS (2008) Overview of artificial neural networks. Methods Mol Biol 458:15–23PubMed
39.
Di Saverio S, Podda M, De Simone B, Ceresoli M, Augustin G, Gori A, Boermeester M, Sartelli M, Coccolini F, Tarasconi A et al (2020) Diagnosis and treatment of acute appendicitis: 2020 update of the WSES Jerusalem guidelines. World J Emerg Surg 15(1):27PubMedPubMedCentralCrossRef
40.
Nie D, Zhan Y, Xu K, Zou H, Li K, Chen L, Chen Q, Zheng W, Peng X, Yu M et al (2023) Artificial intelligence differentiates abdominal Henoch–Schonlein purpura from acute appendicitis in children. Int J Rheum Dis 26(12):2534–2542PubMedCrossRef
41.
42.
43.
Issaiy M, Zarei D, Saghazadeh A (2023) Artificial intelligence and acute appendicitis: a systematic review of diagnostic and prognostic models. World J Emerg Surg 18(1):59PubMedPubMedCentralCrossRef
44.
Sakai S, Kobayashi K, Toyabe S, Mandai N, Kanda T, Akazawa K (2007) Comparison of the levels of accuracy of an artificial neural network model and a logistic regression model for the diagnosis of acute appendicitis. J Med Syst 31(5):357–364PubMedCrossRef
45.
Ghareeb WM, Emile SH, Elshobaky A (2022) Artificial intelligence compared to Alvarado scoring system alone or combined with ultrasound criteria in the diagnosis of acute appendicitis. J Gastrointest Surg 26(3):655–658PubMedCrossRef
46.
Lam A, Squires E, Tan S, Swen NJ, Barilla A, Kovoor J, Gupta A, Bacchi S, Khurana S (2023) Artificial intelligence for predicting acute appendicitis: a systematic review. ANZ J Surg 93(9):2070–2078PubMedCrossRef
47.
Akmese OF, Dogan G, Kor H, Erbay H, Demir E (2020) The use of machine learning approaches for the diagnosis of acute appendicitis. Emerg Med Int 2020:7306435PubMedPubMedCentralCrossRef
48.
Akgul F, Er A, Ulusoy E, Caglar A, Citlenbik H, Keskinoglu P, Sisman AR, Karakus OZ, Ozer E, Duman M et al (2021) Integration of physical examination, old and new biomarkers, and ultrasonography by using neural networks for pediatric appendicitis. Pediatr Emerg Care 37(12):e1075–e1081PubMedCrossRef
49.
Aydin E, Turkmen IU, Namli G, Ozturk C, Esen AB, Eray YN, Eroglu E, Akova F (2020) A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children. Pediatr Surg Int 36(6):735–742PubMedCrossRef
50.
Hsieh CH, Lu RH, Lee NH, Chiu WT, Hsu MH, Li YC (2011) Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery 149(1):87–93PubMedCrossRef
51.
Mijwil MM, Aggarwal K (2022) A diagnostic testing for people with appendicitis using machine learning techniques. Multimed Tools Appl 81(5):7011–7023PubMedPubMedCentralCrossRef
52.
Shikha A, Kasem A (2023) The development and validation of artificial intelligence pediatric appendicitis decision-tree for children 0 to 12 years old. Eur J Pediatr Surg 33(5):395–402PubMedCrossRef
53.
Akbulut S, Yagin FH, Cicek IB, Koc C, Colak C, Yilmaz S (2023) Prediction of perforated and nonperforated acute appendicitis using machine learning-based explainable artificial intelligence. Diagnostics (Basel) 13(6):1173
54.
Xia J, Wang Z, Yang D, Li R, Liang G, Chen H, Heidari AA, Turabieh H, Mafarja M, Pan Z (2022) Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis. Comput Biol Med 143:105206PubMedCrossRef
55.
Phan-Mai TA, Thai TT, Mai TQ, Vu KA, Mai CC, Nguyen DA (2023) Validity of machine learning in detecting complicated appendicitis in a resource-limited setting: findings from Vietnam. Biomed Res Int 2023:5013812PubMedPubMedCentralCrossRef
56.
Reismann J, Romualdi A, Kiss N, Minderjahn MI, Kallarackal J, Schad M, Reismann M (2019) Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: an investigator-independent approach. PLoS ONE 14(9):e0222030PubMedPubMedCentralCrossRef
57.
Reismann J, Kiss N, Reismann M (2021) The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents—a proof of concept study. BMC Pediatr 21(1):268PubMedPubMedCentralCrossRef
58.
Prabhudesai SG, Gould S, Rekhraj S, Tekkis PP, Glazer G, Ziprin P (2008) Artificial neural networks: useful aid in diagnosing acute appendicitis. World J Surg 32(2):305–309; discussion 310–301
59.
Yoldas O, Tez M, Karaca T (2012) Artificial neural networks in the diagnosis of acute appendicitis. Am J Emerg Med 30(7):1245–1247PubMedCrossRef
60.
Park SY, Kim SM (2015) Acute appendicitis diagnosis using artificial neural networks. Technol Health Care 23(Suppl 2):S559-565PubMedCrossRef
61.
Rajpurkar P, Park A, Irvin J, Chute C, Bereket M, Mastrodicasa D, Langlotz CP, Lungren MP, Ng AY, Patel BN (2020) AppendiXNet: deep learning for diagnosis of appendicitis from a small dataset of CT exams using video pretraining. Sci Rep 10(1):3958PubMedPubMedCentralCrossRefADS
62.
Bhangu A, Soreide K, Di Saverio S, Assarsson JH, Drake FT (2015) Acute appendicitis: modern understanding of pathogenesis, diagnosis, and management. Lancet 386(10000):1278–1287PubMedCrossRef
63.
64.
Kang CB, Li XW, Hou SY, Chi XQ, Shan HF, Zhang QJ, Li XB, Zhang J, Liu TJ (2021) Preoperatively predicting the pathological types of acute appendicitis using machine learning based on peripheral blood biomarkers and clinical features: a retrospective study. Ann Transl Med 9(10):835PubMedPubMedCentralCrossRef
65.
Marcinkevics R, Reis Wolfertstetter P, Wellmann S, Knorr C, Vogt JE (2021) Using machine learning to predict the diagnosis, management and severity of pediatric appendicitis. Front Pediatr 9:662183PubMedPubMedCentralCrossRef
66.
Gupta R, Sample C, Bamehriz F, Birch DW (2006) Infectious complications following laparoscopic appendectomy. Can J Surg 49(6):397–400PubMedPubMedCentral
67.
Eickhoff RM, Bulla A, Eickhoff SB, Heise D, Helmedag M, Kroh A, Schmitz SM, Klink CD, Neumann UP, Lambertz A (2022) Machine learning prediction model for postoperative outcome after perforated appendicitis. Langenbecks Arch Surg 407(2):789–795PubMedPubMedCentralCrossRef
68.
Bunn C, Kulshrestha S, Boyda J, Balasubramanian N, Birch S, Karabayir I, Baker M, Luchette F, Modave F, Akbilgic O (2021) Application of machine learning to the prediction of postoperative sepsis after appendectomy. Surgery 169(3):671–677PubMedCrossRef
69.
Ghomrawi HMK, O’Brien MK, Carter M, Macaluso R, Khazanchi R, Fanton M, DeBoer C, Linton SC, Zeineddin S, Pitt JB et al (2023) Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. NPJ Digit Med 6(1):148PubMedPubMedCentralCrossRef
70.
Alramadhan MM, Al Khatib HS, Murphy JR, Tsao K, Chang ML (2022) Using artificial neural networks to predict intra-abdominal abscess risk post-appendectomy. Ann Surg Open 3(2):e168PubMedPubMedCentralCrossRef
Metadata
Title
Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review
Authors
Valentina Bianchi
Mauro Giambusso
Alessandra De Iacob
Maria Michela Chiarello
Giuseppe Brisinda
Publication date
12-03-2024
Publisher
Springer International Publishing
Published in
Updates in Surgery
Print ISSN: 2038-131X
Electronic ISSN: 2038-3312
DOI
https://doi.org/10.1007/s13304-024-01801-x