The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is
automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity
and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition.
We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of
automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23
cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal
smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection
based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The
next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients’
clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear
examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%,
and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading
of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading
is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the
advantages when compared with the conventional artificial identification of confocal microscope corneal images, of being
accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with
fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density
and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate,
objective and quantitative manner.

• Australasian Physical & Engineering Sciences in Medicine