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人工智能:胃肠道癌症的临床应用及未来进展

Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.

作者信息

Akbari Abolfazl, Adabi Maryam, Masoodi Mohsen, Namazi Abolfazl, Mansouri Fatemeh, Tabaeian Seidamir Pasha, Shokati Eshkiki Zahra

机构信息

Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran.

Infectious Ophthalmologic Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

出版信息

Front Artif Intell. 2024 Dec 20;7:1446693. doi: 10.3389/frai.2024.1446693. eCollection 2024.


DOI:10.3389/frai.2024.1446693
PMID:39764458
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11701808/
Abstract

One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.

摘要

全球医疗负担的首要原因之一是胃肠道癌症。胃肠道恶性肿瘤患者的病历、实验室检查结果、X光片、内镜图像、组织样本和病史提供了大量的医学数据。有令人鼓舞的迹象表明,人工智能的出现可以利用这些数据改善胃肠道疾病的治疗。深度学习算法可以快速有效地分析非结构化的高维数据,包括文本、图像和波形,而先进的机器学习方法可以揭示疾病风险因素和表型的新见解。总之,人工智能有可能彻底改变胃肠道癌症治疗的各个方面,如早期检测、诊断、治疗和预后。本文重点介绍了人工智能在该领域的一些潜在应用。此外,我们还讨论了该学科的现状及其未来的潜在发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03c4/11701808/01b066656677/frai-07-1446693-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03c4/11701808/01b066656677/frai-07-1446693-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03c4/11701808/01b066656677/frai-07-1446693-g001.jpg

相似文献

[1]
Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.

Front Artif Intell. 2024-12-20

[2]
Scope of Artificial Intelligence in Gastrointestinal Oncology.

Cancers (Basel). 2021-11-1

[3]
Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons.

Therap Adv Gastroenterol. 2021-6-10

[4]
Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

Best Pract Res Clin Gastroenterol. 2025-2

[5]
Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.

J Transl Med. 2025-4-9

[6]
Artificial intelligence in gastrointestinal endoscopy: The future is almost here.

World J Gastrointest Endosc. 2018-10-16

[7]
Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers.

Cancers (Basel). 2022-8-3

[8]
Artificial intelligence as an emerging technology in the current care of neurological disorders.

J Neurol. 2021-5

[9]
Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases.

Curr Med Chem. 2022

[10]
Machine learning on microbiome research in gastrointestinal cancer.

J Gastroenterol Hepatol. 2021-4

本文引用的文献

[1]
Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer.

Cureus. 2024-2-19

[2]
Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.

Cancers (Basel). 2023-11-13

[3]
Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network.

Genome Med. 2023-11-8

[4]
Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer.

Sci Rep. 2023-10-30

[5]
Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients.

BMC Cancer. 2023-10-17

[6]
Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism.

Bioengineering (Basel). 2023-8-9

[7]
Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Front Oncol. 2023-8-3

[8]
Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

Cell Rep Med. 2023-8-15

[9]
Machine learning applications for early detection of esophageal cancer: a systematic review.

BMC Med Inform Decis Mak. 2023-7-17

[10]
Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.

J Med Syst. 2023-7-11

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