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Related Concept Videos

Assessment of the Abdomen I: Inspection and Auscultation01:25

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Introduction
The abdominal examination is a cornerstone of clinical medicine, serving as a critical tool in diagnosing various gastrointestinal (GI) diseases. It involves a systematic approach that includes inspection and auscultation, each with distinct yet complementary roles in assessing the abdomen. This article will delve into these two primary methods healthcare professionals use to examine the abdomen.
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Assessment of the Abdomen II: Percussion01:18

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Percussion is a fundamental technique used to assess the liver, spleen, and abdominal organs by tapping the abdomen and interpreting the resulting sounds. This method helps identify fluid, distention, and masses through variations in sound, such as the high-pitched tympany of air-filled areas and the dullness of solid masses. Understanding how to percuss these organs provides valuable information for healthcare professionals in diagnosing conditions early.
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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Assessment of the Abdomen III: Palpation01:23

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Palpation is a crucial tactile examination method for assessing abdominal organs and detecting conditions like tenderness, distention, masses, or fluid. It involves both light and deep palpation techniques, each serving specific diagnostic purposes. Light palpation helps identify tenderness and other surface-level indicators, while deep palpation locates and assess abdominal masses and organ boundaries. A skilled professional can gather valuable insights through palpation, including evaluating...
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Updated: Jun 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine learning-based predictive model for abdominal diseases using physical examination datasets.

Wei Chen1, YuJie Zhang2, Weili Wu3

  • 1Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China; Four Provincial Marginal Traditional Chinese Medicine Hospitals (Quzhou Traditional Chinese Medicine Hospital) Affiliated to Zhejiang University of Traditional Chinese Medicine, Quzhou, China.

Computers in Biology and Medicine
|March 26, 2024
PubMed
Summary

This study predicts liver, kidney, and gallbladder diseases using basic physical exam data, offering a novel approach to early diagnosis when ultrasound is unavailable. Machine learning models show high accuracy for conditions like fatty liver.

Keywords:
Abdominal ultrasonographyFatty liverMachine learningPhysical examination data

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Area of Science:

  • Medical Informatics
  • Diagnostic Imaging
  • Preventive Medicine

Background:

  • Abdominal ultrasound is crucial for diagnosing liver, kidney, and gallbladder diseases.
  • Access to abdominal ultrasound is limited by equipment, cost, and time constraints.
  • Predicting these conditions using accessible data is essential for broader screening.

Purpose of the Study:

  • To develop predictive models for liver, kidney, and gallbladder diseases using basic physical examination data.
  • To assess the feasibility of using non-imaging data for early disease risk identification.
  • To enhance early diagnosis and prevention strategies for common abdominal ailments.

Main Methods:

  • Utilized basic physical examination data including demographics, vitals, and blood markers.
  • Developed seven single-label predictive models using the XGBoost algorithm.
  • Established one multi-label predictive model using a Fully Convolutional Network (FCN).

Main Results:

  • XGBoost models achieved high Area Under the Curve (AUC) for fatty liver (0.9344), kidney cysts (0.8241), and liver deposits (0.8221).
  • Other single-label models showed AUCs ranging from 0.7508 to 0.7928 for various liver, gallbladder, and kidney conditions.
  • The multi-label FCN model achieved an AUC of 0.6344 for predicting multiple conditions simultaneously.

Conclusions:

  • Basic physical examination data can effectively predict the risk of various liver, kidney, and gallbladder diseases.
  • These predictive models offer a valuable tool for early disease detection and targeted prevention.
  • Interpretability analysis of the models supports their clinical applicability and enhances understanding.