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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Implementation Determinants of Integrated Tuberculosis and Diabetes Care in South Asian Association for Regional Cooperation (SAARC) Countries: A Systematic Review.

International journal of integrated care·2026
Same author

Chitosan-Modified Gold Nanoparticle-Based Electrochemical Immunosensor for C-Reactive Protein Detection.

Bioengineering (Basel, Switzerland)·2026
Same author

ATML1-GIR1-TPL/TPR transcriptional repression module controls glucosinolates and giant cells in <i>Arabidopsis thaliana</i> sepals.

bioRxiv : the preprint server for biology·2026
Same author

Radiological misdiagnosis of intracranial solitary fibrous tumor: a case report STAT6 immunohistochemistry resolves the diagnostic challenge.

Annals of medicine and surgery (2012)·2026
Same author

Expanding the genetic spectrum of autosomal recessive microcephaly in Pakistani families.

BMC neurology·2026
Same author

Operating Room Extubation in Major Aortic Surgery: Policy Change Leading to Improved Recovery.

Journal of cardiothoracic and vascular anesthesia·2026
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 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

2.7K

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet

Tanaya Kondejkar1, Salah Mohammed Awad Al-Heejawi1, Anne Breggia2

  • 1College of Engineering, Northeastern University, Boston, MA 02115, USA.

Bioengineering (Basel, Switzerland)
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a deep learning approach for accurate prostate cancer grading using ResNet models. The method achieved 0.999 accuracy in identifying clinically significant prostate cancer, improving diagnosis and treatment planning.

Keywords:
health caremachine learningprostate cancer classification

More Related Videos

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.8K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.7K

Related Experiment Videos

Last Updated: Jun 22, 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

2.7K
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.8K
Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

3.7K

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate cancer is a significant health concern requiring early diagnosis and precise treatment.
  • Accurate grading of prostate cancer is crucial for effective intervention and patient outcomes.
  • Current diagnostic methods necessitate improvement for enhanced precision in cancer grading.

Purpose of the Study:

  • To develop and evaluate a deep learning-based approach for prostate cancer grading.
  • To frame prostate cancer grading as a classification problem using advanced computational models.
  • To improve the accuracy of identifying clinically significant prostate cancer for better treatment planning.

Main Methods:

  • Utilized ResNet models for image classification on multi-scale patch-level digital pathology images.
  • Employed the Diagset dataset for training and validation of the proposed deep learning models.
  • Framed the prostate cancer grading task as a binary classification problem.

Main Results:

  • Achieved a high accuracy of 0.999 in identifying clinically significant prostate cancer.
  • Demonstrated the effectiveness of ResNet models in analyzing digital pathology images for cancer grading.
  • Validated the approach on the comprehensive Diagset dataset.

Conclusions:

  • The proposed deep learning approach significantly enhances prostate cancer grading accuracy.
  • This method offers a promising tool for improving early diagnosis and personalized treatment strategies.
  • Integrating AI with digital pathology advances cancer diagnostics and patient care.