| 1. |
- Arora, Anmol, et al.
(författare)
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The value of standards for health datasets in artificial intelligence-based applications
- 2023
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Ingår i: Nature Medicine. - : NATURE PORTFOLIO. - 1078-8956 .- 1546-170X. ; 29, s. 2929-2938
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative). A systematic review, combined with a stakeholder survey, presents an overview of current practices and recommendations for dataset curation in health, with specific focuses on data diversity and artificial intelligence-based applications.
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| 2. |
- Asa, Sylvia, et al.
(författare)
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2020 vision of digital pathology in action
- 2019
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Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 10:27
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
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| 4. |
- Bivik Stadler, Caroline, 1986-, et al.
(författare)
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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
- 2021
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Ingår i: Journal of digital imaging. - : Springer-Verlag New York. - 0897-1889 .- 1618-727X. ; 34, s. 105-115
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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| 5. |
- Bodén, Anna, et al.
(författare)
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The human-in-the-loop : an evaluation of pathologists interaction with artificial intelligence in clinical practice
- 2021
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Ingår i: Histopathology. - : Wiley-Blackwell. - 0309-0167 .- 1365-2559. ; 79:2, s. 210-218
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Tidskriftsartikel (refereegranskat)abstract
- Aims: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied. Methods and results: We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation (eyeballing) (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation. Conclusion: The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.
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| 6. |
- Broad, A., et al.
(författare)
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Attention-guided sampling for colorectal cancer analysis with digital pathology
- 2022
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Ingår i: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Improvements to patient care through the development of automated image analysis in pathology are restricted by the small image patch size that can be processed by convolutional neural networks (CNNs), when compared to the whole-slide image (WSI). Tile-by-tile processing across the entire WSI is slow and inefficient. While this may improve with future computing power, the technique remains vulnerable to noise from uninformative image areas. We propose a novel attention-inspired algorithm that selects image patches from informative parts of the WSI, first using a sparse randomised grid pattern, then iteratively re-sampling at higher density in regions where a CNN classifies patches as tumour. Subsequent uniform sampling across the enclosing region of interest (ROI) is used to mitigate sampling bias. Benchmarking tests informed the adoption of VGG19 as the main CNN architecture, with 79% classification accuracy. A further CNN was trained to separate false-positive normal epithelium from tumour epithelium, in a novel adaptation of a two-stage model used in brain imaging. These subsystems were combined in a processing pipeline to generate spatial distributions of classified patches from unseen WSIs. The ROI was predicted with a mean F1 (Dice) score of 86.6% over 100 evaluation WSIs. Several algorithms for evaluating tumour–stroma ratio (TSR) within the ROI were compared, giving a lowest root mean square (RMS) error of 11.3% relative to pathologists’ annotations, against 13.5% for an equivalent tile-by-tile pipeline. Our pipeline processed WSIs between 3.3x and 6.3x faster than tile-by-tile processing. We propose our attention-based sampling pipeline as a useful tool for pathology researchers, with the further potential for incorporating additional diagnostic calculations. © 2022 The Authors
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| 7. |
- Capitanio, Arrigo, et al.
(författare)
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Digital cytology: A short review of technical and methodological approaches and applications
- 2018
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Ingår i: Cytopathology. - : WILEY. - 0956-5507 .- 1365-2303. ; 29:4, s. 317-325
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Forskningsöversikt (refereegranskat)abstract
- The recent years have been characterised by a rapid development of whole slide imaging (WSI) especially in its applications to histology. The application of WSI technology to cytology is less common because of technological problems related to the three-dimensional nature of cytology preparations (which requires capturing of z-stack information, with an increase in file size and usability issues in viewing cytological preparations). The aim of this study is to provide a review of the literature on the use of digital cytology and provide an overview of cytological applications of WSI in current practice as well as identifying areas for future development.
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| 8. |
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| 9. |
- Clarke, Emily L., et al.
(författare)
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Development and Evaluation of a Novel Point-of-Use Quality Assurance Tool for Digital Pathology
- 2019
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Ingår i: Archives of Pathology & Laboratory Medicine. - : COLL AMER PATHOLOGISTS. - 0003-9985 .- 1543-2165. ; 143:10, s. 1246-1255
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Tidskriftsartikel (refereegranskat)abstract
- Context.-Flexible working at diverse or remote sites is a major advantage when reporting using digital pathology, but currently there is no method to validate the clinical diagnostic setting within digital microscopy. Objective.-To develop a preliminary Point-of-Use Quality Assurance (POUQA) tool designed specifically to validate the diagnostic setting for digital microscopy. Design.-We based the POUQA tool on the red, green, and blue (RGB) values of hematoxylin-eosin. The tool used 144 hematoxylin-eosin-colored, 5x5-cm patches with a superimposed random letter with subtly lighter RGB values from the background color, with differing levels of difficulty. We performed an initial evaluation across 3 phases within 2 pathology departments: 1 in the United Kingdom and 1 in Sweden. Results.-In total, 53 experiments were conducted across all phases resulting in 7632 test images viewed in all. Results indicated that the display, the users visual system, and the environment each independently impacted performance. Performance was improved with reduction in natural light and through use of medical-grade displays. Conclusions.-The use of a POUQA tool for digital microscopy is essential to afford flexible working while ensuring patient safety. The color-contrast test provides a standardized method of comparing diagnostic settings for digital microscopy. With further planned development, the color-contrast test may be used to create a "Verified Login" for diagnostic setting validation.
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| 10. |
- Clarke, Emily L., et al.
(författare)
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Development of a novel tissue-mimicking color calibration slide for digital microscopy
- 2018
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Ingår i: Color Research and Application. - : WILEY. - 0361-2317 .- 1520-6378. ; 43:2, s. 184-197
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Tidskriftsartikel (refereegranskat)abstract
- Digital microscopy produces high resolution digital images of pathology slides. Because no acceptable and effective control of color reproduction exists in this domain, there is significant variability in color reproduction of whole slide images. Guidance from international bodies and regulators highlights the need for color standardization. To address this issue, we systematically measured and analyzed the spectra of histopathological stains. This information was used to design a unique color calibration slide utilizing real stains and a tissue-like substrate, which can be stained to produce the same spectral response as tissue. By closely mimicking the colors in stained tissue, our target can provide more accurate color representation than film-based targets, whilst avoiding the known limitations of using actual tissue. The application of the color calibration slide in the clinical setting was assessed by conducting a pilot user-evaluation experiment with promising results. With the imminent integration of digital pathology into the routine work of the diagnostic pathologist, it is hoped that this color calibration slide will help provide a universal color standard for digital microscopy thereby ensuring better and safer healthcare delivery.
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