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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Videos

Code-free automated machine learning for OCT-based classification of vitreoretinal interface diseases.

Lorenzo Ferro Desideri1,2, Enrico Bernardi3, Carla Troyas4,5

  • 1Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. lorenzoferrodes@gmail.com.

International Journal of Retina and Vitreous
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

Code-free automated machine learning (AutoML) accurately classifies optical coherence tomography (OCT) scans for vitreoretinal interface disorders. This clinician-driven approach shows promise for advancing artificial intelligence in ophthalmology.

Keywords:
Automated machine learningEpiretinal membraneImage classificationMacular holeOptical coherence tomographyVitreoretinal interface

Related Experiment Videos

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Classifying vitreoretinal interface disorders using optical coherence tomography (OCT) requires expert interpretation and can be difficult in ambiguous cases.
  • Automated machine learning (AutoML) platforms offer a potential solution for clinician-led artificial intelligence (AI) development without requiring coding skills.
  • This study investigates the efficacy of a code-free AutoML approach for classifying OCT images.

Purpose of the Study:

  • To evaluate the performance of a code-free AutoML platform for classifying vitreoretinal interface disorders on OCT scans.
  • To assess the accuracy of automated classification compared to expert interpretation.

Main Methods:

  • A cross-sectional image classification study utilized 434 manually labeled OCT B-scans from public datasets.
  • Images were categorized into four groups: epiretinal membrane (ERM), lamellar macular hole (LMH), full-thickness macular hole (MH), and normal retina.
  • A cloud-based AutoML platform (Google Cloud Vertex AI) handled data splitting, model training, and optimization, with performance measured by precision, recall, and average precision.

Main Results:

  • The AutoML model achieved high overall performance with an average precision of 0.988 and precision/recall of 97.6%.
  • Macular hole (MH) and normal retina classifications achieved perfect precision and recall (100%).
  • Epiretinal membrane (ERM) showed 100% precision and 92.9% recall, while lamellar macular hole (LMH) had 100% recall and 83.3% precision, with misclassifications limited to related anatomical entities.

Conclusions:

  • Code-free AutoML facilitates accurate OCT-based classification of vitreoretinal disorders through a clinician-driven workflow.
  • This technology has the potential to increase AI adoption in ophthalmology.
  • It can also expedite the prototyping of AI tools for clinical research.