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: May 28, 2026

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
07:08

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Published on: January 10, 2019

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Md Mahmudul Hasan1, Jack Phu2,3,4,5, Henrietta Wang2,3,5

  • 1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia. md_mahmudul.hasan@unsw.edu.au.

Scientific Reports
|May 26, 2026
PubMed
Summary

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

Hepatoprotective Effects of Baccaurea motleyana Fruit Extract Against CCl<sub>4</sub>-Induced Liver Injury in Rats: In Vivo Evaluation and GC-MS Phytochemical Profiling.

Chemistry & biodiversity·2026
Same author

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Bioactivity Assessment and In Silico Target Engagement of <i>Clerodendrum infortunatum</i> Extracts: Correlating Experimental Outcomes With DFT and Docking Predictions.

Food science & nutrition·2026
Same author

Eco sustainable IoT based roof garden monitoring and planting recommendation system with machine learning.

Scientific reports·2026
Same author

Agreement between ganglion cell-inner plexiform layer metrics from widefield optical coherence tomography and Goldmann II, III, and V in glaucoma.

Optometry and vision science : official publication of the American Academy of Optometry·2026
Same author

Polymeric and Chelate Gel Precursors for Transition Metal Oxide and Silicon-Based Anodes in Lithium-Ion Batteries.

Gels (Basel, Switzerland)·2026
Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026
Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026
Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026
Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026
Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026
Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026
See all related articles
This summary is machine-generated.

Explainable AI enhances neuropathy diagnosis by analyzing optical coherence tomography (OCT) scans. This privacy-preserving tool accurately identifies conditions like glaucoma and dementia, outperforming human clinicians.

Area of Science:

  • Ophthalmology
  • Neuroscience
  • Artificial Intelligence

Background:

  • Glaucoma shares pathological similarities with neurodegenerative diseases such as dementia and Parkinson's disease.
  • Current diagnostic methods for neuropathy often exclude neurodegenerative cases and utilize "black box" models lacking transparency.
  • Optical coherence tomography (OCT) is a key imaging modality for ocular health assessment.

Purpose of the Study:

  • To develop a reliable, explainable, and privacy-preserving diagnostic tool for neuropathy using OCT data.
  • To compare the diagnostic performance of machine learning models against experienced clinicians.
  • To integrate explainable AI (XAI) techniques for enhanced model interpretability in neuropathy diagnosis.

Main Methods:

  • Applied explainable machine learning algorithms to OCT data from patients with glaucoma, dementia, Parkinson's disease, ischaemic optic neuropathy (ION), and normal controls.
Keywords:
DementiaExplainable machine learningGlaucomaIschaemic optic neuropathyOptical coherence tomographyParkinson’s diseasePartial dependency analysisPerimetrySHAP analysis

Related Experiment Videos

Last Updated: May 28, 2026

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
07:08

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Published on: January 10, 2019

  • Extracted spatial and frequency domain features, followed by feature selection and hierarchical classification.
  • Utilized SHapley Additive exPlanations (SHAP) and partial dependency analysis for model interpretability, incorporating a differential privacy mechanism.

    • Main Results:

    • The explainable AI model achieved an area under the curve (AUC) of 0.90 for classifying neuropathy.
    • Machine learning models demonstrated superior diagnostic accuracy compared to clinicians, with a 26.3% higher accuracy for neuropathy and 24.8% for glaucoma.
    • The privacy-preserving approach effectively safeguarded patient data.

    Conclusions:

    • Explainable AI, combined with OCT imaging, offers a promising approach to enhance diagnostic support for neuropathy and glaucoma.
    • The integration of privacy-preserving mechanisms is crucial for the clinical adoption of AI-driven diagnostic tools.
    • Machine learning models show potential to augment clinical decision-making, improving diagnostic accuracy and efficiency.