Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.

BACKGROUND: This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements. METHODS: A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience. RESULTS: The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality. CONCLUSION: The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. METHODS: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. RESULTS: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. CONCLUSIONS: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD.

RATIONALE AND OBJECTIVES: To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score. RESULTS: 38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set. CONCLUSION: The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

Classification of diabetic retinopathy: Past, present and future.

Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide. Since DR was first recognized as an important complication of diabetes, there have been many attempts to accurately classify the severity and stages of disease. These historical classification systems evolved as understanding of disease pathophysiology improved, methods of imaging and assessing DR changed, and effective treatments were developed. Current DR classification systems are effective, and have been the basis of major research trials and clinical management guidelines for decades. However, with further new developments such as recognition of diabetic retinal neurodegeneration, new imaging platforms such as optical coherence tomography and ultra wide-field retinal imaging, artificial intelligence and new treatments, our current classification systems have significant limitations that need to be addressed. In this paper, we provide a historical review of different classification systems for DR, and discuss the limitations of our current classification systems in the context of new developments. We also review the implications of new developments in the field, to see how they might feature in a future, updated classification.

Severity staging of chronic obstructive pulmonary disease: differences in pre- and post-bronchodilator spirometry.

PURPOSE: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines for chronic obstructive pulmonary disease (COPD) uses the post-bronchodilator spirometry for diagnosis and severity staging. We evaluated differences in the severity classification of COPD, based on pre- and post-bronchodilator spirometry. MATERIALS AND METHODS: From 2000 to 2004, 207 COPD patients who underwent spirometry before and after inhalation of 400 microg of fenoterol were analyzed. A responder to the bronchodilator test (BDT) was defined by the American Thoracic Society (ATS) as an increase in forced expiratory volume in one second (FEV(1)) or forced vital capacity > or = 12% and > or = 200 mL, and by the European Respiratory Society (ERS) as an increase in FEV(1) > or = 10% of the predicted value. COPD severity was classified according to the 2008 GOLD guidelines. RESULTS: For the entire study population, the FEV(1) increased by 11.8 +/- 12.5% of baseline after BDT and 41.1% and 27.1% of subjects were classified as responders using the ATS and ERS criteria, respectively. Based on pre-BDT spirometry, 55, 85, 58, and 9 patients were classified as Stage I-IV COPD, respectively. Sixty-seven (32.4%) patients changed severity staging after BDT, including 20.0%, 28.2%, 44.8%, and 66.7% of pre-BDT patients Stages I through IV, respectively. More ATS or ERS BDT-responders had a change in severity staging than non-responders (52.9% vs. 18.9% and 62.5% vs. 21.2%, both p < 0.001). CONCLUSION: Our data suggest that the severity staging of COPD using pre-BDT spirometry might lead to significant differences as compared to staging, based on post-BDT spirometry, as recommended by the current GOLD guidelines.

Severity Staging of Chronic Obstructive Pulmonary Disease: Differences in Pre- and Post-Bronchodilator Spirometry

PURPOSE: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines for chronic obstructive pulmonary disease (COPD) uses the post-bronchodilator spirometry for diagnosis and severity staging. We evaluated differences in the severity classification of COPD, based on pre- and post-bronchodilator spirometry. MATERIALS AND METHODS: From 2000 to 2004, 207 COPD patients who underwent spirometry before and after inhalation of 400 microg of fenoterol were analyzed. A responder to the bronchodilator test (BDT) was defined by the American Thoracic Society (ATS) as an increase in forced expiratory volume in one second (FEV1) or forced vital capacity > or = 12% and > or = 200 mL, and by the European Respiratory Society (ERS) as an increase in FEV1 > or = 10% of the predicted value. COPD severity was classified according to the 2008 GOLD guidelines. RESULTS: For the entire study population, the FEV1 increased by 11.8 +/- 12.5% of baseline after BDT and 41.1% and 27.1% of subjects were classified as responders using the ATS and ERS criteria, respectively. Based on pre-BDT spirometry, 55, 85, 58, and 9 patients were classified as Stage I-IV COPD, respectively. Sixty-seven (32.4%) patients changed severity staging after BDT, including 20.0%, 28.2%, 44.8%, and 66.7% of pre-BDT patients Stages I through IV, respectively. More ATS or ERS BDT-responders had a change in severity staging than non-responders (52.9% vs. 18.9% and 62.5% vs. 21.2%, both p < 0.001). CONCLUSION: Our data suggest that the severity staging of COPD using pre-BDT spirometry might lead to significant differences as compared to staging, based on post-BDT spirometry, as recommended by the current GOLD guidelines.