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Gallbladder01:17

Gallbladder

270
The gallbladder is a small, pear-shaped organ that plays a crucial role in our digestive system. Measuring about 10 cm in length, it is comparable in size to a kiwi fruit and is located in a hollow area on the lower surface of the liver. The gallbladder's primary function is to store and concentrate bile, a fluid produced by the liver that aids in digestion.
The gallbladder's anatomy consists of three regions: the fundus, body, and neck. Extending from the neck, the cystic duct joins...
270
Diseases of the Liver and Gallbladder01:26

Diseases of the Liver and Gallbladder

409
Liver and gallbladder diseases are a significant health concern, with prominent conditions including cirrhosis, hepatitis, non-alcoholic fatty liver disease (NAFLD), and gallstones. Jaundice is a common manifestation of liver and biliary disease.
Cirrhosis is characterized by the scarring of hepatic lobules in the liver, which are replaced by fibrous tissue, affecting the liver's normal functioning. NAFLD, on the other hand, is caused by an excessive build-up of fat in the liver, not...
409
Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
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Endoscopic Procedures V: ERCP01:26

Endoscopic Procedures V: ERCP

53
Endoscopic Retrograde Cholangiopancreatography (ERCP) is a diagnostic procedure that combines endoscopy and fluoroscopy to diagnose and treat conditions related to the bile ducts, pancreatic ducts, and gallbladder. This procedure is beneficial for identifying and addressing blockages, gallstones, strictures, and tumors within the biliary or pancreatic systems. ERCP is both diagnostic and therapeutic, offering the ability to visualize and treat identified problems in one session.
Patient...
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Related Experiment Video

Updated: May 22, 2025

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation
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Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System.

Ahmet Bozdag1, Muhammed Yildirim2, Mucahit Karaduman3

  • 1Department of General Surgery, School of Medicine, Firat University, Elazığ 23119, Turkey.

Diagnostics (Basel, Switzerland)
|March 13, 2025
PubMed
Summary

Artificial intelligence (AI) aids in early gallbladder disease detection using Content-Based Image Retrieval (CBIR). This AI model accurately identifies gallbladder diseases, improving diagnostic efficiency.

Keywords:
CBIRartificial intelligencecarcinomacholecystitisgallstone diseases

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Diagnostic Tools

Background:

  • Early detection of gallbladder (GB) diseases is crucial for better patient outcomes.
  • Delayed or inaccurate diagnosis of GB diseases can lead to poorer clinical results and increased symptoms.
  • Interpreting ultrasound images for GB diseases requires specialized expertise, and manual analysis is time-consuming and resource-intensive, particularly in remote areas.

Purpose of the Study:

  • To develop and evaluate an AI-powered Content-Based Image Retrieval (CBIR) system for the early detection of gallbladder diseases.
  • To improve the efficiency and accuracy of diagnosing GB diseases compared to traditional methods.

Main Methods:

  • Utilized artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), with a focus on Content-Based Image Retrieval (CBIR).
  • The developed CBIR model integrates features from three distinct pre-trained architectures for feature extraction.
  • Employed the cosine method as the similarity measurement metric for image comparison.

Main Results:

  • The proposed CBIR model demonstrated superior performance when compared to six other Convolutional Neural Network (CNN) models and two textural models.
  • Achieved an Average Precision (AP) value of 0.94, indicating high accuracy in disease detection.
  • The model successfully combined features from multiple pre-trained architectures for effective feature extraction.

Conclusions:

  • The developed CBIR model shows significant potential for the early detection and diagnosis of gallbladder diseases.
  • The high AP value confirms the model's efficacy and reliability in identifying GB diseases.
  • This AI-driven approach offers a promising solution for overcoming the challenges associated with manual ultrasound image interpretation.