Validation and refinement of two interpretable models for coronavirus disease 2019 prognosis prediction

Objective: To validate two proposed coronavirus disease 2019 (COVID-19) prognosis models, analyze the characteristics of different models, consider the performance of models in predicting different outcomes, and provide new insights into the development and use of artificial intelligence (AI) predictive models in clinical decision-making for COVID-19 and other diseases. Materials and Methods: We compared two proposed prediction models for COVID-19 prognosis that use a decision tree and logistic regression modeling. We evaluated the effectiveness of different model-building strategies using laboratory tests and/or clinical record data, their sensitivity and robustness to the timings of records used and the presence of missing data, and their predictive performance and capabilities in single-site and multicenter settings. Results: The predictive accuracies of the two models after retraining were improved to 93.2% and 93.9%, compared with that of the models directly used, with accuracies of 84.3% and 87.9%, indicating that the prediction models could not be used directly and require retraining based on actual data. In addition, based on the prediction model, new features obtained by model comparison and literature evidence were transferred to integrate the new models with better performance. Conclusions: Comparing the characteristics and differences of datasets used in model training, effective model verification, and a fusion of models is necessary in improving the performance of AI models

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Effunet-spagen: An efficient and spatial generative approach to glaucoma detection

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho