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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

Kivanc Kose¹, Alican Bozkurt², Christi Alessi-Fox³

¹Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

The Journal of Investigative Dermatology

|December 16, 2019

Summary

A new quality assurance method uses machine learning to automatically detect uninformative areas in reflectance confocal microscopy (RCM) images. This ensures diagnostic quality for remote RCM image analysis, improving patient care.

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Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

In vivo reflectance confocal microscopy (RCM) provides detailed skin lesion imaging, reducing biopsy needs.
The expanding RCM workflow includes offsite image interpretation, necessitating robust quality assurance.
Objective quality control is crucial for RCM images when patients are not present during interpretation.

Purpose of the Study:

To develop and validate an automated quality assurance process for RCM imaging.
To quantify diagnostically uninformative areas within skin lesions using RCM and dermoscopy images.
To assess the efficacy of a machine learning model for objective quality control in RCM.

Main Methods:

A pixel-level segmentation model was trained and validated on 117 RCM mosaics.
The model was tested on 372 coregistered RCM-dermoscopic image pairs.
A multimodal approach combining RCM and dermoscopy was evaluated to improve quantification of uninformative regions.

Main Results:

The RCM-only model achieved 82% sensitivity and 93% specificity in delineating uninformative areas.
The multimodal approach demonstrated potential for enhancing the quantification of uninformative regions.
Machine learning-based automatic quantification was identified as a feasible quality control measure.

Conclusions:

Automated quantification of uninformative areas in RCM images is a feasible objective quality control measure.
Integrating dermoscopy with RCM can improve the accuracy of quality assessment.
This approach supports reliable remote diagnosis in the evolving RCM workflow.