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Cited by 48Retinal Imaging and AnalysisArtificial Intelligence in HealthcareRetinal Diseases and Treatments

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Open access
Fraccaro, Paolo, Nicolo, Massimo, Bonetto, Monica, Giacomini, Mauro, Weller, Peter, Traverso, Carlo Enrico, Prosperi, Mattia, OSullivan, Dympna·2015·BMC Ophthalmology
DOI 10.1186/1471-2415-15-10PMC PMC4417241
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Abstract

BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). METHODS: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. RESULTS: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. CONCLUSIONS: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

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  • Missing data were usually ignored by dropping data or imputing, and if not dealt with appropriately can skew the results.

    “Missing data were usually ignored by dropping data or imputing, if not dealt with appropriately can skew the results68,106,173.”

    From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [68]

    The Methods and Results describe this study's use of complete-case analysis and several imputation approaches. The Discussion says model performance did not change significantly across imputation methods. The paper does not establish that missing data are usually handled this way or that inappropriate handling skewed its results. (LLM, conf=0.82)

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  • Fraccaro et al.'s study of macular degeneration noted their white box performed as well as black box methods.

    “Contrast this with the work of Fraccaro et al. whose study of macular degeneration noted their white box performed as well as black box methods implementions68.”

    From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [68]

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  1. The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2415/15/10/prepub

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Authors
Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C. E., Prosperi, M., & OSullivan, D.
Affiliations
  • City, University of London (GB)
  • University of Genoa (IT)
  • University of Manchester (GB)
ORCID
Venue
BMC Ophthalmology
Publisher
Springer Science and Business Media LLC
Published
2015-01-27
Volume
15
Issue
1
Pages
10-10
ISSN
1471-2415 (ISSN-L)
DOI
10.1186/1471-2415-15-10
PMC
PMC4417241
OpenAlex
W2136493825
Cited by
48
References
39
Citations by year
2025: 3 · 2024: 7 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 8 · 2019: 3 · 2018: 6 · 2017: 4 · 2016: 3 · 2015: 1
FWCI
3.78
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Ophthalmology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (publishedVersion)
  • NORMA (acceptedVersion)
  • TRAP@NCI (National College of Ireland) (acceptedVersion)
  • City Research Online (City University London) (acceptedVersion)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Keywords
age-related macular degeneration, automated diagnosis, machine learning, interpretable models, black-box models, soft drusen, electronic health records, random forest, support vector machine, logistic regression, decision tree, AdaBoost, clinical decision support
MeSH terms
Machine Learning, Aged, Decision Trees, Female, Humans, Image Processing, Computer-Assisted, Macula Lutea, Macular Degeneration, Male, Retinal Hemorrhage, ROC Curve, Sensitivity and Specificity, Retinal Drusen, Area Under Curve, Retinal Pigment Epithelium, Subretinal Fluid

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