In the era of artificial intelligence, the technology of optical character recognition under complex backgrounds has become particularly important. This article investigated how machinelearningalgorithms can improve...
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In the era of artificial intelligence, the technology of optical character recognition under complex backgrounds has become particularly important. This article investigated how machinelearningalgorithms can improve the accuracy of text recognition in complex scenarios. By analyzing algorithms such as scale-invariant feature transform, K-means clustering, and support vector machine, a system was constructed to address the challenges of text recognition under complex backgrounds. Experimental results show that the proposed algorithm achieves 7.66% higher accuracy than traditional algorithms, and the built system is fast, powerful, and highly satisfactory to users, with a 13.6% difference in results between the two groups using different methods. This indicates that the method proposed in this study can effectively meet the needs of complex text recognition, significantly improving recognition efficiency and user satisfaction.
Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to...
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Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machinelearning (ML), since up to 30% of patients do not benefit. Methods: We retrospectively collected all consecutive patients with PD-L1 > 50 % metastatic NSCLC treated with first-line ICI at our institution between 2017 and 2021. Demographic, laboratory, molecular and clinical data were retrieved manually or automatically according to data sources. Primary aim was to explore feasibility of ML models in clinical routine setting and to detect problems and solutions for everyday implementation. Early progression was used as preliminary endpoint to test our algorithm. Results: Out of 123 patients, 106 were included, 52/106 (49 %) had disease progression or died within 3 months of start of ICI. Early progression correlated with increased neutrophil percentage (>80 % of white blood cells), neutrophil/lymphocyte ratio (>8) and lower-range PD-L1 status (<70 %) at baseline, which was consistent with literature. automated ML (AutoML) models run on our dataset reached precision scores around 80 %, with Voting Ensemble emerging as best performing model, while white-box models (such as Shapley Additive exPlanations) provided better explainability. In all AutoML models, laboratory features were the top selected features, whilst clinical ones needed more pre-processing before gaining relevance, which was consistent with different data extraction (automatic versus manual) and missing data rates. Conclusions: ML models' application is feasible in clinical practice and can trustworthily predict early progression during first-line ICI for metastatic NSCLC. Solving
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