Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Deep Learning in Medical Image Analysis.

Heang-Ping Chan1, Ravi K Samala2, Lubomir M Hadjiiski2

  • 1Department of Radiology, University of Michigan, Ann Arbor, MI, USA. chanhp@umich.edu.

Advances in Experimental Medicine and Biology
|February 8, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Corrigendum to 'Synthesis and structure-activity relationships of N - (3 - (1H-imidazol-2-yl) phenyl) - 3-phenylpropionamide derivatives as a novel class of covalent inhibitors of p97/VCP ATPase' [ Eur. J. Med. Chem. 248 (2023) 115094].

European journal of medicinal chemistry·2026
Same author

Screens and Youth Sleep.

JAMA pediatrics·2026
Same author

Highly ionic-dispersed oxygen electrode for reversible proton ceramic electrochemical cells.

Nature communications·2026
Same author

Dictamnine Inhibits WNT Pathway and EMT Progression in Prostate Cancer and Remodels the Tumor Microenvironment.

Cancers·2026
Same author

Gut microbiota and macrophage crosstalk: implications for colitis-associated colorectal cancer.

Frontiers in cellular and infection microbiology·2026
Same author

Impact of Order Set Design on Pediatric Intravenous Fluid Guideline Adherence: A National Study.

Hospital pediatrics·2026
Same journal

Peptidomics in the Spotlight: Advanced Sample Treatment Techniques and Analytical Insights.

Advances in experimental medicine and biology·2026
Same journal

Methods for the Investigation of Protein-Ligands Interactions.

Advances in experimental medicine and biology·2026
Same journal

Sample Preparation Strategies for Microbial Cell Surface Proteomics: Integrating Shaving and Shotgun Approaches.

Advances in experimental medicine and biology·2026
Same journal

Proteomic Sample Preparation for the Petroleum Industry: A Biocorrosion Case Study.

Advances in experimental medicine and biology·2026
Same journal

Proteomic and Functional Comparison of Extracellular Vesicles from Wild-Type and Lyn-Deficient Stromal Cells.

Advances in experimental medicine and biology·2026
Same journal

Proteomic Analysis of Histone Sequence Variants and Post-translationally Modified Forms.

Advances in experimental medicine and biology·2026
See all related articles

Deep learning and artificial intelligence (AI) show promise for revolutionizing healthcare, particularly in medical image analysis for computer-aided diagnosis (CAD). However, challenges remain in developing and integrating these AI tools into clinical practice for reliable patient care.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Medical Image Analysis

Background:

  • Deep learning is a leading machine learning technique with significant success in pattern recognition.
  • Artificial intelligence (AI) holds potential for transformative changes in healthcare.
  • Early applications in lesion detection show superior performance over conventional methods and even radiologists.

Purpose of the Study:

  • To explore the potential of deep learning in medical image analysis for computer-aided diagnosis (CAD).
  • To discuss the challenges in developing and implementing AI-driven CAD tools in clinical settings.
  • To outline efforts required for robust AI tool development and clinical workflow integration.

Main Methods:

  • Review of deep learning applications in medical image analysis.
Keywords:
Artificial intelligenceBig dataComputer-aided diagnosisDeep learningInterpretable AIMachine learningMedical imagingQuality assuranceTransfer learningValidation

Related Experiment Videos

Last Updated: Dec 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
  • Discussion of challenges in clinical translation of AI tools.
  • Exploration of strategies for integrating AI into clinical workflows.

    • Main Results:

    • Deep learning demonstrates high performance in lesion detection and classification tasks.
    • Significant research and development are focused on AI for CAD systems.
    • Clinical implementation of AI tools faces practical and technical hurdles.

    Conclusions:

    • Deep learning offers promising advancements for computer-aided diagnosis (CAD) and decision support.
    • Overcoming challenges in development and integration is crucial for realizing AI's potential in patient care.
    • Robust, reliable AI tools are needed to support clinicians and enhance healthcare delivery.