Preliminary clinical application of fluorescence microscopic imaging and computer-aided diagnosis system in the diagnosis of superficial cutaneous fungal infections

Abstract

Objective To evaluate the accuracy of automated fluorescence microscopic imaging and computer-aided diagnosis system (AFMICADS) in the auxiliary diagnosis of superficial cutaneous fungal infections. Methods Totally, 106 outpatients and inpatients with suspected superficial fungal infections were enrolled from clinical departments of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between July 2018 and September 2018. A total of 126 specimens were collected, including 83 skin scales and 43 nail parings. Each specimen was divided into 3 groups to be examined by conventional fungal microscopy, culture with modified Sabouraud dextrose agar and fluorescence microscopy (artificial fluorescence microscopy and AFMICADS-based fluorescence microscopy) respectively. A positive result was defined as that conventional fungal microscopy and/or fungal culture was positive. Consistency rate, sensitivity and specificity of the 3 microscopic methods were calculated. Statistical analysis was carried out with SPSS 10.0 software by using McNemar test and Kappa test for analyzing difference in the positive rate, as well as consistency, between the 3 microscopic methods and the positive standard, and by using efficiency test for comparing the consistency rate among the 3 microscopic methods. Results Of 126 specimens, 124 (98.4%) were positive for artificial fluorescence microscopy, and 123 (97.6%) for AFMICADS-based fluorescence microscopy. Both positive rates of the above 2 microscopic methods were significantly higher than the positive rate of the positive standard (77.8%, both P 0.05) . Conclusion The accuracy of AFMICADS-based fluorescence microscopy in the diagnosis of superficial cutaneous fungal infections is similar to that of artificial fluorescence microscopy. Key words: Fungi; Microscopy, fluorescence; Diagnosis, computer-assisted; Fluorescence stain; Machine vision; Automated fluorescence microscopic imaging