Predicting Success in Descemet Membrane Endothelial Keratoplasty Surgery Using Machine Learning.

PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics. METHODS: Several machine learning methods were trained to predict GF automatically. To predict GF, the following variables were obtained: donor age, sex, systemic diseases, medications, duration of stay in the intensive care unit, death-to-preservation time (DPT), endothelial cell density of the cornea, tightness of Descemet membrane roll during surgery, anterior chamber tamponade, tamponade used for rebubbling, and preoperative best corrected visual acuity. Five classification methods were experimented with the study data set: random forest, support vector machine, k-nearest neighbor, RUSBoosted tree, and neural networks. In holdout validation, 75% of the data were used in training and the remaining 25% used in testing. The predictive accuracy, sensitivity, specificity, f-score, and area under the receiver operating characteristic curve of the methods were evaluated. RESULTS: The highest classification accuracy achieved during the experiments was 96%. The precision, recall, and f1-score values were 0.95, 0.81, and 0.90, respectively. Feature importance was also computed using analysis of variance. The model revealed that GF risk was related to DPT and the intensive care unit duration (P < 0.05). No significant relationship was found between donor age, endothelial cell density, systemic diseases and medications, graft roll, tamponades, and GF risk. CONCLUSIONS: This study shows a strong relationship between increased intensive care duration, DPT, and GF. Experimental results demonstrate that machine learning methods may effectively predict GF automatically.

Is there any convergence in the CO2 emission efficiency of airlines?

This paper examines the CO2 emission efficiency of airlines in the years 2011 and 2018 by using the Atmosfair Airline Index. This index gives reliable results since it encompasses data from more than 100 airlines and considers important variables in the calculation of CO2 emissions. Firstly, we investigate the regional differences and the effect of the share of government ownership in the CO2 emission efficiency of airlines. These factors have not been taken into account in other studies by using such a comprehensive index. Secondly, by utilizing the Barro and Sala-i Martin model that is commonly used to examine the regional income convergence model in economics, we also check whether there is a convergence in the CO2 emission efficiency of airlines or not. As a result, in terms of efficiency growth, we find that airlines in Europe are more successful compared to airlines from other regions. Furthermore, increases in the share of government ownership in airlines negatively affect the CO2 emission efficiency in Asia, whereas it is insignificant in Europe and America. Moreover, there is no convergence in the CO2 emission efficiency of airlines from all regions. This shows that low-efficient airlines are not catching up with high-efficient airlines. Lastly, we find that charter airlines are more efficient in terms of CO2 emissions.

Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model.

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.

Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics.

BACKGROUND AND OBJECTIVES: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance. METHODS: Numerous methods have been proposed to classify malignancy, but there are not many studies that facilitate nodule characteristics in classification steps. In this study, we use information on nodule characteristics and propose cascaded classification schemes. A group of hand-crafted features and deep features are used to define the nodules. In the first step of the classifier, the nodule characteristics are classified based on individual base classifiers. In the second step, the results of the first level classifier are combined for use in malignancy classification. In addition, stacking methods are applied to improve the performance of the cascaded classifiers. RESULTS: The results confirmed that combining deep and hand-crafted features contribute to classification performance with an 8% improvement in average classification accuracy, 9% improvement in sensitivity, and 3% in specificity. Deep features from a nodule bounding area are more descriptive than the exact nodule region. The best performing cascaded classifier featured a classification accuracy of 84.70%, sensitivity of 67.37%, and specificity of 95.46%. First level stacking demonstrated similar results on classification accuracy and specificity but sensitivity was measured at 75.59%. Stacking on both levels provided the best classification accuracy and specificity with scores of 86.98% and 96.06%, respectively. When the malignancy ratings were grouped, stacking on both levels demonstrated better performance than other methods with a classification accuracy of 88.80%, sensitivity of 88.41%, and specificity of 94.12%. CONCLUSIONS: Information on cascading characteristics with image features is beneficial for the classification of the malignancy ratings. Stacking approaches on both levels demonstrate better classification accuracy, but in the context of sensitivity, first level stacking performs better. Grouping the malignancy ratings results in better classification outcomes as in the case of similar studies in the literature.

Cyclotorsion measurement using scleral blood vessels.

BACKGROUND AND OBJECTIVES: Measurements of the cyclotorsional movement of the eye are crucial in refractive surgery procedures. The planned surgery pattern may vary substantially during an operation because of the position and eye movements of the patient. Since these factors affect the outcome of an operation, eye registration methods are applied in order to compensate for errors. While the majority of applications are based on features of the iris, we propose a registration method which uses scleral blood vessels. Unlike previous offline techniques, the proposed method is applicable during surgery. METHODS: The sensitivity of the proposed registration method is tested on an artificial benchmark dataset involving five eye models and 46,305 instances of eye images. The cyclotorsion angles of the dataset vary between -10° and +10° at 1° intervals. Repeated measurements and ANOVA and Cochran's Q tests are applied in order to determine the significance of the proposed method. Additionally, a pilot study is carried out using data obtained from a commercially available device. The real data are validated using manual marking by an expert. RESULTS AND CONCLUSIONS: The results confirm that the proposed method produces a smaller error rate (mean = 0.44 ± 0.41) compared to the existing method in [1] (mean = 0.64 ± 0.58). A further conclusion is that feature extraction algorithms affect the results of the proposed method. The SIFT (mean = 0.74 ± 0.78), SURF64 (mean = 0.56 ± 0.46), SURF128 (mean = 0.57 ± 0.48) and ASIFT (mean = 0.29 ± 0.25) feature extraction algorithms were examined; the ASIFT method was the most successful of these algorithms. Scleral blood vessels are observed to be useful as a feature extraction region due to their textural properties.

A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.

Predicting malignancy of solitary pulmonary nodules from computer tomography scans is a difficult and important problem in the diagnosis of lung cancer. This paper investigates the contribution of nodule characteristics in the prediction of malignancy. Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting malignancy of pulmonary nodules. LIDC database contains CT scans of nodules and information about nodule characteristics evaluated by multiple annotators. In the first step of our method, votes for nodule characteristics are obtained from ensemble classifiers by using image features. In the second step, votes and rules obtained from radiologist evaluations are used by a weighted rule based method to predict malignancy. The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between malignancy and other nodule characteristics and agreement ratio of radiologists are considered in rule evaluation. To handle the unbalanced nature of LIDC, ensemble classifiers and data balancing methods are used. The proposed approach is compared with the classification methods trained on image features. Classification accuracy, specificity and sensitivity of classifiers are measured. The experimental results show that using nodule characteristics for malignancy prediction can improve classification results.