• Machine Learning for Diagnosis in Automated Glaucoma Clinics

    By Lynda Seminara
    Selected By: Deepak P. Edward, MD

    Journal Highlights


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    The demand for global glaucoma services is growing rapidly, driven by factors such as longer life expectancy, earlier diagnosis, and the plethora of available treatments. Machine learning is a powerful tool for analyzing patient data, and various computer algorithms can distinguish between glaucomatous signs and neurological field defects. In a glaucomatous population, Thomas et al. explored the utility of feed-forward back-propagation artificial neural networks (ANNs) for detecting field defects caused by pituitary disease. They found that by inspecting bilateral field representations, traditional ANNs are efficient in detecting chiasmal field de­fects. The findings suggest that machine learning may play a key “diagnostic oversight” role in the future.

    For this study, the researchers obtained 24-2 Humphrey visual field reports for 121 patients with pituitary disease and 907 patients with glau­comatous findings, and they used optical character recognition to extract threshold values from the reports. For each patient, left- and right-eye visual fields were coupled in an array to create bilateral field representations. ANNs were created to detect chiasmal field defects. The authors then assessed the networks’ ability to identify a single pituitary field among the 907 glauco­matous distractors.

    Results of the analyses showed that mean field thresholds in all locations were lower for the pituitary group (20.3 dB, standard deviation [SD] = 5.2 dB) than for the glaucoma group (24.4 dB, SD = 5.0 dB), suggesting that the degree of field loss is greater in patients with pituitary disease (p < .0001). However, the substantial overlap in results im­plies that mean bilateral field loss alone is not a reliable indicator of etiology.

    Overall, ANNs performed well in the discrimination task; sensitivity and specificity exceeded 95%. When a single pituitary field was hidden among 907 glaucomatous fields, it had one of the five top indexes of suspicion in 91% of the ANNs.

    The advent of telemedicine means that better methods are needed to au­tomatically identify not only glaucoma but also any accompanying pathology or masquerading conditions, the authors said. They added that ANNs such as theirs may have a role to play in the automated care of the future.

    The original article can be found here.