Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Protein Glycosylation in Helicobacter pylori: Beyond the Flagellins?

Glycosylation of flagellins by pseudaminic acid is required for virulence in Helicobacter pylori. We demonstrate that, in H. pylori, glycosylation extends to proteins other than flagellins and to sugars other than pseudaminic acid. Several candidate glycoproteins distinct from the flagellins were detected via ProQ-emerald staining and DIG- or biotin- hydrazide labeling of the soluble and outer membrane fractions of wild-type H. pylori, suggesting that protein glycosylation is not limited to the flagellins. DIG-hydrazide labeling of proteins from pseudaminic acid biosynthesis pathway mutants showed that the glycosylation of some glycoproteins is not dependent on the pseudaminic acid glycosylation pathway, indicating the existence of a novel glycosylation pathway. Fractions enriched in glycoprotein candidates by ion exchange chromatography were used to extract the sugars by acid hydrolysis. High performance anion exchange chromatography with pulsed amperometric detection revealed characteristic monosaccharide peaks in these extracts. The monosaccharides were then identified by LC-ESI-MS/MS. The spectra are consistent with sugars such as 5,7-diacetamido-3,5,7,9-tetradeoxy-L-glycero-L-manno-nonulosonic acid (Pse5Ac7Ac) previously described on flagellins, 5-acetamidino-7-acetamido-3,5,7,9-tetradeoxy-L-glycero-L-manno-nonulosonic acid (Pse5Am7Ac), bacillosamine derivatives and a potential legionaminic acid derivative (Leg5AmNMe7Ac) which were not previously identified in H. pylori. These data open the way to the study of the mechanism and role of protein glycosylation on protein function and virulence in H. pylori

Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Ethics of artificial intelligence in radiology:summary of the joint European and North American multisociety statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes