Dominguez, Luis

Ganju, Nakul

Ganju, Shaunak

Meltzer, Andrew

English

George Washington University: Health Sciences Research Commons (HSRC)

Himmelfarb Health Sciences Library, The George Washington University Health Sciences Research Commons URGENT Matters School of Medicine and Health Sciences 12-7-2023 The AI Revolution: Transforming Upper Gastrointestinal Endoscopy Nakul Ganju George Washington University Shaunak Ganju George Washington University Luis Dominguez George Washington University Andrew Meltzer George Washington University Follow this and additional works at: https://hsrc.himmelfarb.gwu.edu/smhs_URGENT_Matters  Part of the Medicine and Health Sciences Commons Recommended Citation Ganju, Nakul; Ganju, Shaunak; Dominguez, Luis; and Meltzer, Andrew, "The AI Revolution: Transforming Upper Gastrointestinal Endoscopy" (2023). URGENT Matters. Paper 38. https://hsrc.himmelfarb.gwu.edu/smhs_URGENT_Matters/38 This News Article is brought to you for free and open access by the School of Medicine and Health Sciences at Health Sciences Research Commons. It has been accepted for inclusion in URGENT Matters by an authorized administrator of Health Sciences Research Commons. For more information, please contact hsrc@gwu.edu. The AI Revolution: Transforming Upper Gastrointestinal EndoscopyNakul Ganju, Shaunak Ganju, Luis Dominguez, MD & Andrew Meltzer, MD12/07/2023While Esophagogastroduodenoscopy (EGD) is the gold standard in diagnosing uppergastrointestinal (UGI) disease, nearly 11.3% of UGI cancers are still missed on the initial EGD.1Artificial Intelligence (AI) has been proposed as an aid in improving lesion detection andreducing the number of undetected cancers. Compared to human interpretation, AI programs areresistant to factors like fatigue and human error. Current AI programs rely on Machine Learning(ML), a generalized concept of training algorithms using data to recognize patterns and makepredictions. Deep Learning (DL) is a subset of ML, that uses a deep layered neural network tomake accurate insights in pictures, texts, videos, and other forms of data. The most common DLmethod used in endoscopy relies on convolutional neural networks (CNN), which are specializedin image classification and recognition. In the context of EGDs, AI computer systems can bedivided into computer-assisted detection (CADe) for detecting lesions and computer-assisteddiagnosis (CADx) for lesion characterization/classification. AI systems also can offer guidanceon endoscopy performance and quality control via mapping.One of the core benefits of CNN imaging models is the ability to assist in detecting anddiagnosing various UGI cancers and their premalignant conditions. CNN models can increase theaccuracy of detection and lower physicians' workload in interpretation during an EGD.Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) areesophageal cancers (EC) of the upper two-thirds and lower third of the esophagus, respectively.For both, detection usually occurs in the late stages when the prognosis is very poor.7,8Detectionof early neoplasia in Barrett's esophagus (BE) is crucial to a good prognosis for EAC. CADemodels for BE and early EAC have shown great promise with high sensitivity and specificity.4,5One model tested by Ebigdo et al. showed an accuracy of 90%.6For ESCC, CADe models appearto have an 87-96% accuracy.9When predicting invasion depth using CADx models, studies showaccuracy is around 68-89%.5,9For both ESCC and EAC detection, however, evidence remainsmixed on whether AI models can outperform physicians.5Early Gastric Cancer (EGC) is anothercommon cancer screened during EGDs. Studies for CADe models of EGC typically showaccuracy ranging from 85-99%, matching or beating physician interpretations.5Overall, manystudies are limited by their small size and retrospective nature; however, results are generallypositive for the accuracy of AI models in detecting and diagnosing UGI cancers.Aside from CADx and CADe systems, AI can also improve the quality of the EGDprocedure and guide physicians through the process. Blind spots in EGD remain an issue, andusing real-time AI monitoring, blind spots could be detected with a 90% accuracy.12 Suggestionsfor image documentation of landmarks are absent in the American Society for GastrointestinalEndoscopy (ASGE) guidelines; however, several international organizations suggest their use forquality control.10 AI can assist in identifying anatomical locations, with AI models showingaccuracy for anatomical classification of around 97%.11 As physicians have moved to incorporateAI into the EGD procedures, studies on CADx systems have also demonstrated improved patientsatisfaction post-procedure.2While the potential of AI use in EGD shows promise, limitations and concerns exist. AnAI model is only as good as the data used to train it, and many of the AI models for EGDs indevelopment rely on high-quality images that do not reflect realistic conditions. Concerns aboutthe possibility of human biases, such as bias for race or gender, might arise if the training datadoes not include datasets from a diverse patient group. Privacy issues associated with collectinglarge volume data further complicate the development of accurate models to train the AIalgorithms.Currently, prospective studies with large patient groups are needed to prove AI solutions'efficacy and whether AI use definitively reduces medical costs and improves patient satisfaction.Aside from studies on efficacy and feasibility, concerns also exist with the AI interface andwhether it can be physician friendly. The mechanisms behind ML and DL programs are complex,and whether AI solutions fit physician workflow for widespread adoption remains to be seen.While the current goal of CADx programs is to assist physicians, concerns arise at the possibilityof making diagnoses without a physician. While the topic remains speculation, this potential forautomated use potentially creates ethical and legal complications.In addition to assisting the performance of EGDs, AI also has the potential torevolutionize how the GI tract is visualized through pill endoscopy. Research is underway foringestible capsule endoscopes to be autonomously driven by AI software to survey the entirestomach and document any abnormalities.12,13,14In summary, AI has the potential to revolutionize EGD procedures, improve the qualityof EGDs, and detect and diagnose various UGI diseases. Large prospective studies performed inreal-time settings are still needed to prove such solutions' efficacy. Such studies should alsoconsider experience levels when comparing physician and AI interpretations. As AI use in EGDbecomes more commonly used, organizations like the ASGE will likely need to developstandards for clinical use of AI tools.The authors have no conflicts to report.References:1. Turshudzhyan A, Rezaizadeh H, Tadros M. Lessons learned: Preventable misses andnear-misses of endoscopic procedures. World J Gastrointest Endosc.2022;14(5):302-310. doi:10.4253/wjge.v14.i5.3022. Xu C, Zhu Y, Wu L, et al. Evaluating the effect of an artificial intelligence system on theanesthesia quality control during gastrointestinal endoscopy with sedation: arandomized controlled trial. BMC Anesthesiol. 2022;22(1):313. Published 2022Oct 7. doi:10.1186/s12871-022-01796-13. Sharma P, Hawes RH, Bansal A, et al. Standard endoscopy with random biopsies versusnarrow band imaging targeted biopsies in Barrett's oesophagus: a prospective,international, randomised controlled trial. Gut. 2013;62(1):15-21.doi:10.1136/gutjnl-2011-3009624. Bhatti KM, Khanzada ZS, Kuzman M, Ali SM, Iftikhar SY, Small P. DiagnosticPerformance of Artificial Intelligence-Based Models for the Detection of EarlyEsophageal Cancers in Barret's Esophagus: A Meta-Analysis of Patient-BasedStudies. Cureus. 2021;13(6):e15447. Published 2021 Jun 4.doi:10.7759/cureus.154475. Tokat M, van Tilburg L, Koch AD, Spaander MCW. Artificial Intelligence in UpperGastrointestinal Endoscopy. Dig Dis. 2022;40(4):395-408.doi:10.1159/0005182326. Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in theevaluation of cancer in Barrett's oesophagus. Gut. 2020;69(4):615-616.doi:10.1136/gutjnl-2019-3194607. van Putten M, de Vos-Geelen J, Nieuwenhuijzen GAP, et al. Long-term survivalimprovement in oesophageal cancer in the Netherlands. Eur J Cancer.2018;94:138-147. doi:10.1016/j.ejca.2018.02.0258. Portale G, Hagen JA, Peters JH, et al. Modern 5-year survival of resectable esophagealadenocarcinoma: single institution experience with 263 patients. J Am Coll Surg.2006;202(4):588-598. doi:10.1016/j.jamcollsurg.2005.12.0229. Pan Y, He L, Chen W, Yang Y. The current state of artificial intelligence in endoscopicdiagnosis of early esophageal squamous cell carcinoma. Front Oncol.2023;13:1198941. Published 2023 May 24. doi:10.3389/fonc.2023.119894110. Marques S, Bispo M, Pimentel-Nunes P, Chagas C, Dinis-Ribeiro M. ImageDocumentation in Gastrointestinal Endoscopy: Review of Recommendations. GEPort J Gastroenterol. 2017;24(6):269-274. doi:10.1159/00047773911. Chen D, Wu L, Li Y, et al. Comparing blind spots of unsedated ultrafine, sedated, andunsedated conventional gastroscopy with and without artificial intelligence: aprospective, single-blind, 3-parallel-group, randomized, single-center trial.Gastrointest Endosc. 2020;91(2):332-339.e3. doi:10.1016/j.gie.2019.09.01612. Song YQ, Mao XL, Zhou XB, et al. Use of Artificial Intelligence to Improve the QualityControl of Gastrointestinal Endoscopy. Front Med (Lausanne). 2021;8:709347.Published 2021 Jul 22. doi:10.3389/fmed.2021.70934713. Meltzer AC, Kumar A, Kallus SJ, et al. Magnetically controlled capsule for assessmentof the gastric mucosa in symptomatic patients: A prospective, single-arm,single-center, comparative study. iGIE. 2023;2(2):139-147. doi:10.1016/j.igie.2023.04.007.14. Wang X, Hu X, Xu Y, Yong J, Li X, Zhang K, Gan T, Yang J, Rao N. A systematicreview on diagnosis and treatment of gastrointestinal diseases by magneticallycontrolled capsule endoscopy and artificial intelligence. Therap AdvGastroenterol. 2023 Oct 27;16:17562848231206991. doi:10.1177/17562848231206991. PMID: 37900007; PMCID: PMC10612444.

The AI Revolution: Transforming Upper Gastrointestinal Endoscopy

https://hsrc.himmelfarb.gwu.edu/cgi/viewcontent.cgi?article=1037&context=smhs_URGENT_Matters

The AI Revolution: Transforming Upper Gastrointestinal Endoscopy

Abstract

Similar works

Full text

Available Versions

George Washington University: Health Sciences Research Commons (HSRC)