RESUMO
Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.
RESUMO
Although still in its infancy, artificial intelligence (AI) analysis of kidney biopsy images is anticipated to become an integral aspect of renal histopathology. As these systems are developed, the focus will understandably be on developing ever more accurate models, but successful translation to the clinic will also depend upon other characteristics of the system.In the extreme, deployment of highly performant but "black box" AI is fraught with risk, and high-profile errors could damage future trust in the technology. Furthermore, a major factor determining whether new systems are adopted in clinical settings is whether they are "trusted" by clinicians. Key to unlocking trust will be designing platforms optimized for intuitive human-AI interactions and ensuring that, where judgment is required to resolve ambiguous areas of assessment, the workings of the AI image classifier are understandable to the human observer. Therefore, determining the optimal design for AI systems depends on factors beyond performance, with considerations of goals, interpretability, and safety constraining many design and engineering choices.In this article, we explore challenges that arise in the application of AI to renal histopathology, and consider areas where choices around model architecture, training strategy, and workflow design may be influenced by factors beyond the final performance metrics of the system.
Assuntos
Inteligência Artificial , Confiança , Humanos , RimRESUMO
Human artificial chromosomes (HACs), which carry a fully functional centromere and are maintained as a single-copy episome, are not associated with random mutagenesis and offer greater control over expression of ectopic genes on the HAC. Recently, we generated a HAC with a conditional centromere, which includes the tetracycline operator (tet-O) sequence embedded in the alphoid DNA array. This conditional centromere can be inactivated, loss of the alphoid(tet-O) (tet-O HAC) by expression of tet-repressor fusion proteins. In this report, we describe adaptation of the tet-O HAC vector for gene delivery and gene expression in human cells. A loxP cassette was inserted into the tet-O HAC by homologous recombination in chicken DT40 cells following a microcell-mediated chromosome transfer (MMCT). The tet-O HAC with the loxP cassette was then transferred into Chinese hamster ovary cells, and EGFP transgene was efficiently and accurately incorporated into the tet-O HAC vector. The EGFP transgene was stably expressed in human cells after transfer via MMCT. Because the transgenes inserted on the tet-O HAC can be eliminated from cells by HAC loss due to centromere inactivation, this HAC vector system provides important novel features and has potential applications for gene expression studies and gene therapy.
