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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Updated: Jun 14, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain,

Muhammad Umar Nasir1, Safiullah Khan2, Shahid Mehmood1

  • 1Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning system accurately detects osteosarcoma (a bone cancer) from medical images. This AI approach enhances early diagnosis, improving patient outcomes and data security using blockchain and edge computing.

Keywords:
IoMTblockchainedge computingfog computingosteosarcoma cancertransfer learning

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Osteosarcoma is a malignant bone tumor typically affecting long bones in children.
  • Manual detection is expertise-driven and time-consuming, impacting early diagnosis and mortality rates.
  • Automated analysis of medical images offers potential for faster, more efficient osteosarcoma detection.

Purpose of the Study:

  • To develop and evaluate a deep learning-based system for automatic osteosarcoma detection using whole slide images (WSIs).
  • To enhance diagnostic efficiency and accuracy in identifying this bone malignancy.
  • To ensure patient data privacy and system efficiency through integrated technologies.

Main Methods:

  • A deep learning model was trained and tested on a large dataset of WSIs for osteosarcoma classification.
  • Blockchain technology was implemented to safeguard patient data integrity and privacy.
  • Edge and fog computing were utilized to optimize system performance and reduce server load.

Main Results:

  • The deep learning system achieved up to 99.3% accuracy in detecting osteosarcoma from WSIs.
  • The integration of blockchain and edge/fog computing enhanced data security and operational efficiency.
  • The system demonstrates high potential for reliable and rapid automated osteosarcoma diagnosis.

Conclusions:

  • Deep learning models can effectively automate osteosarcoma detection from WSIs with high accuracy.
  • Blockchain and edge/fog computing integration offers a secure, efficient, and privacy-preserving solution for AI-driven medical diagnostics.
  • This automated approach has the potential to significantly improve early detection rates and patient management for osteosarcoma.