Methods

Our study included 54 patients with FES, 48 individuals at clinical high risk (CHR) for developing psychosis, and 73 healthy controls (HCs). The device we designed is shown in figure 1. The subject’s forearm is placed on the arm tray while holding the crossbar (figure 1A). Then, aqueous methylnicotinate is applied simultaneously in four dilutions (0.1 M, 0.01 M, 0.001 M, and 0.0001 M) to the inner forearm skin using filter paper patches. The patches are removed after 60 seconds. Five minutes after the removal, the camera above the tray takes four photographs of the arm at 5-minute intervals with the fill lights turned on (figure 1B). This way, the photographs are collected from a fixed vertical view in a well-lit environment, controlling data quality for the subsequent analysis.

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Figure 1

Structure diagram of the device used to measure niacin skin-flushing.

Generally, healthy people are more reactive to the niacin flush effects than patients with FES (figure 2A vs figure 2B). However, situations also occur where the reactions of healthy individuals and patients with FES are indistinguishable by visual judgement. Our diagnostic algorithm explained below overcomes this ambiguity by using statistical features to evaluate the ‘redness’ of the niacin flush response.

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Figure 2

Niacin skin flush response.

We began by detecting the reaction areas using a convolutional neural network. Then, we quantified the images of the reaction areas (figure 3A,B) through the YCbCr colour system (figure 3C): Y, Cb, and Cr indicate the brightness, blue-difference, and red-difference chrominance components of each image, respectively. By encoding the ‘redness’ with the Cr parameter, we marked the area containing the erythema using the Cr values (figure 3D,E). We then collected the Cr values of individuals’ erythema, standardised by their unique, individual skin colours. Because patients with FES have standardised Cr distributions that are distinct from HCs (figure 3F), we could find a ‘hyperplane separation’ -- which works as a classifier for diagnosis -- between patients and HCs based on the difference in those distributions. More specifically, we treated each individual’s Cr density as the input and a numerical prediction result as the output, with ‘one’ indicating ‘FES’ and ‘zero’ indicating ‘healthy’. We then searched for a set of weights projecting from the input to the output by minimising the prediction error when classifying patients with FES versus HCs. The weights obtained through this minimisation were then applied to generate diagnostic results for the early psychosis screening of individuals with CHR, based upon their skin reaction images. Since such predicted outputs do not precisely equal zero or one numerically, we define the diagnostic result as ‘FES’ if the predicted value is larger than 0.5 and ‘healthy’ otherwise.

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Figure 3

Niacin flush response evaluation. FES, first episode schizophrenia.

To justify the validity of our algorithm, we estimated a set of weights using the FES and HC groups; then, we performed a blind test using the 48 individuals with CHR that included 10 conversion cases and 38 non-conversion cases. The prediction results, the positive predictive value (PPV), the negative predictive value (NPV), the sensitivity, and the specificity are displayed in tables 1 and 2. For the in-sample performance within the FES and HC groups, all 54 patients with FES and 69 of the 73 HCs were correctly identified, as shown in table 1. For the out-of-sample performance in the group with CHR, all 10 conversion cases and 33 of the 38 non-conversion cases were correctly predicted, as shown in table 2.

The algorithm also embodies a self-improvement feature. As more individuals get tested, we can collect the traditional diagnoses by follow-up. Then, the classifier can be kept updated by constantly estimating the hyperplane separation with the enlarging sample.

The algorithm described above is installed in our device, along with the classifier derived from the FES and HC samples. When diagnosing individuals for FES in the future, the device could automatically perform an analysis in which the predetermined classifier would generate a test report based on the individual’s standardised Cr density obtained from the forearm images. We have attached a short video as an online supplemental file to illustrate the implementation of this device.