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ObjectivesTo develop and externally geographically validate a mixed-effects deep learning model to diagnose COVID-19 from computed tomography (CT) imaging following best practice guidelines and assess the strengths and weaknesses of deep learning COVID-19 diagnosis. DesignModel development and external validation with retrospectively collected data from two countries. SettingHospitals in Moscow, Russia, collected between March 1, 2020, and April 25, 2020. The China Consortium of Chest CT Image Investigation (CC-CCII) collected between January 25, 2020, and March 27, 2020. Participants1,110 and 796 patients with either COVID-19 or healthy CT volumes from Moscow, Russia, and China, respectively. Main outcome measuresWe developed a deep learning model with a novel mixed-effects layer to model the relationship between slices in CT imaging. The model was trained on a dataset from hospitals in Moscow, Russia, and externally geographically validated on a dataset from a consortium of Chinese hospitals. Model performance was evaluated in discriminative performance using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, calibration performance was assessed using calibration curves, and clinical benefit was assessed using decision curve analysis. Finally, the models decisions were assessed visually using saliency maps. ResultsExternal validation on the large Chinese dataset showed excellent performance with an AUROC of 0.936 (95%CI: 0.910, 0.961). Using a probability threshold of 0.5, the sensitivity, specificity, NPV, and PPV were 0.753 (0.647, 0.840), 0.909 (0.869, 0.940), 0.711 (0.606, 0.802), and 0.925 (0.888, 0.953), respectively. ConclusionsDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models. StatementsThe authors do not own any of the patient data, and ethics approval was not needed. The lead author affirms that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. Patients and the public were not involved in the study. FundingThis study was funded by EPSRC studentship (No. 2110275), EPSRC Impact Acceleration Account (IAA) funding, and Amazon Web Services. SummaryO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIDeep learning can diagnose diseases from imaging data automatically C_LIO_LIMany studies using deep learning are of poor quality and fail to follow current best practice guidelines for the development and reporting of predictive models C_LIO_LICurrent methods do not adequately model the relationship between slices in CT volumetric data C_LI What this study addsO_LIA novel method to analyse volumetric imaging data composed of slices such as CT images using deep learning C_LIO_LIModel developed following current best-practice guidelines for the development and reporting of prediction models C_LI
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Objective To construct an objective analysis system of corneal nerve tortuosity and detect the changes of corneal subbasal nerve tortuosity in patients with dry eye and diabetes. Methods GradeⅠtoⅣnerve tortuosity were evaluated and 80 photos of each grade were randomly chosen from the in vivo confocal microscopy library. Nerve fibers were extracted,segmented and then analyzed by 6 tortuosity related parameters including L C, Seg L C mean,Cur mean,Specific p,ICM and SCC mean. After verifying the validaty of parameters above,a cross-sectional study was conducted. Subjects were collected from June,2018 to February,2019 in Peking University Third Hospital,and were divided into healthy control group (28 persons 56 eyes),dry eye without diabetes group (28 patients 56 eyes),diabetes without dry eye group(24 patients 48 eyes),diabetes with dry eye group (23 patients 46 eyes) . Basic and dry eye information includes sex,age,ocular surface disease index ( OSDI) ,tear film break-up time (TBUT),Schirmer Ⅰ test (SⅠt) and corneal fluorescence staining (CFS) score. Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) were detected in diabetic patients. Cochet-Bonnet examination (C-BE) was detected to evaluate corneal sensation and 2 corneal subbasal nerve photos of each eye were selected for effective tortuosity and density related parameters analysis. Data was analyzed by SPSS and diagnostic test were perfomed by MedCalc. This study followed the Declaration of Helsinki. This study protocol was approved by Ethic Committee of Peking University Third Hospital ( No. IRB00006761-M2017354 ) . Written informed consent was obtained from each subject prior to entering study cohort. Results L C,Seg L C mean,Cur mean,Specific p,ICM and SCC mean increased as the nerve tortuosity increased from Grade Ⅰ to Grade Ⅳ,with an overall significance among 4 groups (F=39. 100, 36. 367,57. 743,4. 043,6. 818,33. 493;all at P<0. 01). Among the above 6 parameters,Cur mean and L C of any two groups were of significant difference (all at P<0. 01). Twenty three to twenty eight persons were enrolled in each group of the cross-sectional study. Sex and age were comparable among 4 groups. Diagnostic criteria were met in dry eye and diabetes. Corneal sensation parameter C-BE decreased in diabetes without dry eye group and diabetes with dry eye group compared with healthy control group ( all at Adj P<0. 05 ) , other than in dry eye without diabetes group (AdjP≥0. 05). Nerve density of diabetes without dry eye group and diabetes with dry eye group was lower compared with healthy control group(all at P<0. 001),while no significant difference between dry eye without diabetes group and healthy control group(P≥0. 05). Among the effective parameters of tortuosity,L C,Cur mean,Seg L C mean and SCC mean of dry eye without diabetes group,diabetes without dry eye group,diabetes with dry eye group were higher compared with healthy control group ( all at P<0. 05 ) . Diagnostic tests of tortuosity related parameters all showed an area under curve (AUC) from 0. 5 to 0. 7. Conclusions L C and Cur mean can be used to analyze corneal nerve curvature more reliably. Compared with normal volunteers,patients of dry eye or diabetes show higher corneal subbasal nerve tortuosity.
