RESUMO
Objective:To establish an artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning optical coherence tomography (OCT) and evaluate its application value.Methods:Diagnostic test studies. From 2016 to 2019, 25 000 OCT images of 25 000 patients treated at the Eye Center of the Second Affiliated Hospital of Zhejiang University School of Medicine were used as training sets and validation sets for the fundus intelligent assisted diagnosis system. Among them, macular epiretinal membrane (MERM), macular edema, macular hole, choroidal neovascularization (CNV), and age-related macular degeneration (AMD) were 5 000 sheets each. The training set and the verification set are 18 124 and 6 876 sheets, respectively. Through the transfer learning Attention ResNet structure algorithm, the OCT image was characterized by lesion identification, the disease feature was extracted by a specific procedure, and the given image was distinguished from other types of disease according to the statistical characteristics of the target lesion. The model algorithms of MERM, macular edema, macular hole, CNV and AMD were initially formed, and the fundus intelligent auxiliary diagnosis system of five models was established. The performance of each model-assisted diagnosis in the fundus intelligent auxiliary diagnostic system was evaluated by applying the subject working characteristic curve, area under the curve (AUC), sensitivity, and specificity.Results:With the intelligent auxiliary diagnosis system, the diagnostic sensitivity of the MERM was 93.5%, the specificity was 99.23%, and AUC was 0.983 7; the diagnostic sensitivity of macular edema was 99.02%, the specificity was 98.17%, and AUC was 0.994 6; the diagnostic sensitivity of macular hole was 98.91%, the specificity was 99.91%, AUC was 0.996 2; the diagnostic sensitivity of CNV was 97.54%, the specificity was 94.71%, AUC was 0.987 5; the diagnostic sensitivity of AMD was 95.12%, the specificity was 97.09%, AUC was 0.985 3.Conclusions:The artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning for OCT images has accurate and efficient diagnostic performance for assisting the diagnosis of MERM, macular edema, macular hole, CNV, and AMD.
RESUMO
Objective: To investigate the cochlear function of the patients suffering from obstructive sleep apnea-hypopnea syndrome(OSAHS) by using ABR.Methods: 40 patients with OSAHS(80 ears) admitted from February 2004 to September 2004 were divided into two subgroups: mild OSAHS group(11 cases) and moderate and severe OSAHS group(29 cases)according to the value of AHI.Each group was tested by Auditory brainstem response(ABR).The latencies of wave Ⅰ、Ⅲ、Ⅴand the interpeak latency intervals of waveⅠ-Ⅲ、Ⅲ-Ⅴ、Ⅰ-Ⅴ were measured.The obtained results of the two subgroups were respectively compared with the normal values.Results: The latencies of waveⅠ、Ⅲ、Ⅴ and the interpeak latency intervals of wave Ⅰ-Ⅲ、Ⅰ-Ⅴin the moderate and severe OSAHS subgroup were prolonged compared with the normal control values,and the difference was significant statistically.The latencies of waveⅠ、Ⅲ in the moderate and severe OSAHS subgroup were significantly prolonged compared with those in the mild OSAHS subgroup.But there was no statistical change in the latencies of wave Ⅰ、Ⅲ、Ⅴand the interpeak latency intervals of waveⅠ-Ⅲ、Ⅲ-Ⅴ、Ⅰ-Ⅴin the mild OSAHS subgroup as compared with the normal control values.Conclusion: Cochlear function can be impaired in patients with moderate and severe OSAHS,and ABR can be taken as a monitor index to observe whether OSAHS affects cochlear function or not.