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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
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Related Experiment Video

Updated: Sep 16, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

264

Artificial Intelligence and Rectal Cancer: Beyond Images.

Tommaso Novellino1, Carlotta Masciocchi2, Andrada Mihaela Tudor1

  • 1Department of Medicine and Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy.

Cancers
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) models analyzing non-image data, like text or numerical data, show significant promise for understanding cancer variability. This review highlights the underappreciated importance of non-image AI inputs in rectal cancer research and clinical practice.

Keywords:
artificial intelligencebig datacombined modelsdeep learningdigital medicineelectronic health recordsimagesmachine learningmultivariate modelspersonalized medicineprecision medicinepredictive modelsreal-world datarectal cancerstructured dataunstructured data

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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology Data Science

Background:

  • Medical big data and cancer variability present challenges.
  • Artificial intelligence (AI) models can process diverse data types, including images, numerical data, categories, and free text.
  • Existing literature often overemphasizes image-based AI models, neglecting other crucial data sources.

Purpose of the Study:

  • To review artificial intelligence models, focusing on non-image data inputs.
  • To evaluate the performance and representation of non-image and combined AI models in medical research, using rectal cancer as a case study.

Main Methods:

  • A comprehensive literature search was performed using PubMed and Scopus.
  • Searches were conducted without temporal limits and in English.
  • Filters were applied to secondary literature for relevant studies.

Main Results:

  • AI models were categorized into image-based, non-image-based, and combined (hybrid) types.
  • Non-image AI models demonstrated significant performance, challenging the focus on image-based approaches.
  • Combined models often outperformed unimodal models, though multicenter validation studies for non-image and combined models are under-represented.

Conclusions:

  • This review is the first to focus on non-image AI inputs in medical research, alone or combined with images.
  • Non-image data components warrant greater attention in research and clinical applications.
  • Multimodality, extending beyond imaging, is crucial for rectal cancer and potentially other diseases.