Progress Toward Poliomyelitis Eradication - Pakistan, January 2023-June 2024.

Since its launch in 1988, the Global Polio Eradication Initiative has made substantial progress toward the eradication of wild poliovirus (WPV), including eradicating two of the three serotypes, and reducing the countries with ongoing endemic transmission of WPV type 1 (WPV1) to just Afghanistan and Pakistan. Both countries are considered a single epidemiologic block. Despite the occurrence of only a single confirmed WPV1 case during the first half of 2023, Pakistan experienced widespread circulation of WPV1 over the subsequent 12 months, specifically in the historical reservoirs of the cities of Karachi, Peshawar, and Quetta. As of June 30, 2024, eight WPV1 cases had been reported in Pakistan in 2024, compared with six reported during all of 2023. These cases, along with more than 300 WPV1-positive environmental surveillance (sewage) samples reported during 2023-2024, indicate that Pakistan is not on track to interrupt WPV1 transmission. The country's complex sociopolitical and security environment continues to pose formidable challenges to poliovirus elimination. To interrupt WPV1 transmission, sustained political commitment to polio eradication, including increased accountability at all levels, would be vital for the polio program. Efforts to systematically track and vaccinate children who are continually missed during polio vaccination activities should be enhanced by better addressing operational issues and the underlying reasons for community resistance to vaccination and vaccine hesitancy.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.