ABSTRACT
BACKGROUND: Manual measurement of endobronchial optical coherence tomography (EB-OCT) images means a heavy workload in the clinical practice, which can also introduce bias if the subjective opinions of doctors are involved. OBJECTIVE: We aim to develop a convolutional neural network (CNN)-based EB-OCT image analysis algorithm to automatically identify and measure EB-OCT parameters of airway morphology. METHODS: The ResUNet, MultiResUNet, and Siamese network were used for analyzing airway inner area (Ai), airway wall area (Aw), airway wall area percentage (Aw%), and airway bifurcate segmentation obtained from EB-OCT imaging, respectively. The accuracy of the automatic segmentations was verified by comparing with manual measurements. RESULTS: Thirty-three patients who were diagnosed with asthma (n = 13), chronic obstructive pulmonary disease (COPD, n = 13), and normal airway (n = 7) were enrolled. EB-OCT was performed in RB9 segment (lateral basal segment of the right lower lobe), and a total of 17,820 OCT images were collected for CNN training, validation, and testing. After training, the Ai, Aw, and airway bifurcate were readily identified in both normal airway and airways of asthma and COPD. The ResUNet and the MultiResUNet resulted in a mean dice similarity coefficient of 0.97 and 0.95 for Ai and Aw segmentation. The accuracy Siamese network in identifying airway bifurcate was 96.6%. Bland-Altman analysis indicated there was a negligible bias between manual and CNN measurements for Ai (bias = -0.02 to 0.01, 95% CI = -0.12 to 0.14) and Aw% (bias = -0.06 to 0.12, 95% CI = -1.98 to 2.14). CONCLUSION: EB-OCT imaging in conjunction with ResUNet, MultiResUNet, and Siamese network could automatically measure normal and diseased airway structure with an accurate performance.
Subject(s)
Asthma , Deep Learning , Pulmonary Disease, Chronic Obstructive , Humans , Tomography, Optical Coherence , Pulmonary Disease, Chronic Obstructive/diagnostic imaging , Lung , Asthma/diagnostic imagingABSTRACT
OBJECTIVE: To study the correlation of azoospermia and severe oligozoospermia with Y chromosome microdeletions, chromosome karyotype and reproductive hormones in male infertility patients. METHODS: We collected semen samples from 63 patients with azoospermia, 49 with severe oligozoospermia and 60 men with normal semen parameters, and determined the incidence of Y chromosome microdeletions, chromosome karyotypes and the levels of reproductive hormones. RESULTS: The incidence rate of Y chromosome microdeletions was 11.11% in the azoospermia and 8.16% in the severe oligozoospermia patients, as compared with 0 in the normal controls (P<0.05). The rate of chromosome abnormalities was 9.52% in the azoospermia group, with statistically significant differences from the severe oligozoospermia and normal control men (both 0) (P<0.05). The levels of FSH and LH were significantly higher in the azoospermia ([20.41 +/- 19.34] IU/L and [11.44 +/- 9.48] IU/L) and the severe oligozoospermia patients ([8.88 +/- 7.04] IU/L and [6.78 +/- 3.85] IU/L) than in the normal males ([3.88 +/- 2.21] IU/L and [4.63 +/- 1.51] IU/L) (P<0.05). CONCLUSION: Examinations of genetics and reproductive hormones are necessary for infertile males with azoospermia and severe oligozoospermia, which may contribute to early diagnosis and treatment.
