International evaluation of an AI system for breast cancer screening

McKinney, Scott Mayer; Sieniek, Marcin; Godbole, Varun; Godwin, Jonathan; Антропова, Н. В.; Ashrafian, Hutan; Back, Trevor; Chesus, Mary; Corrado, G.; Darzi, Ara; Etemadi, Mozziyar; Garcia-Vicente, Florencia; Gilbert, Fiona J.; Halling‐Brown, Mark; Hassabis, Demis; Jansen, Sunny; Karthikesalingam, Alan; Kelly, Christopher; King, Dominic; Ledsam, Joseph R.; Melnick, David S.; Mostofi, Hormuz; Peng, Lily; Reicher, Joshua J.; Romera-Paredes, Bernardino; Sidebottom, Richard; Suleyman, Mustafa; Tse, Daniel; Young, Kenneth C.; Fauw, Jeffrey De; Shetty, Shravya

doi:10.1038/s41586-019-1799-6

Cited by 1,806 publications

(1,306 citation statements)

References 57 publications

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“…Studies using various algorithms have shown human-like or superhuman performance under conditions that are sometimes fairly realistic. [6][7][8][9] In The Lancet Digital Health, Hyo-Eun Kim and colleagues' multireader study provides new insights in the field. 10 In short, the South Korean academic-industrial partnership behind the study trained, validated, and tested a promising AI algorithm to aid detection of breast cancer in mammography.…”

Section: Evaluating Ai In Breast Cancer Screening: a Complex Taskmentioning

confidence: 99%

Evaluating AI in breast cancer screening: a complex task

Dustler

2020

The Lancet Digital Health

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Section: Evaluating Ai In Breast Cancer Screening: a Complex Taskmentioning

confidence: 99%

Evaluating AI in breast cancer screening: a complex task

Dustler

2020

The Lancet Digital Health

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“…Artificial intelligence (AI) is increasingly visible of in our daily lives and ranges from voice recognition on smart speakers (e.g., Amazon's Alexa), to discovering new music from streaming applications that predict new artists for the listener (e.g., Spotify), and computer detection of cancer in mammograms [1]. AI uses mathematical tools, "machine learning," to iteratively learn patterns within training data and when these patterns are found in new data, the AI translates this into a decision, for example, cancer versus not cancer (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence in Infection Prevention

Fitzpatrick

Doherty

Lacey

2020

Curr Treat Options Infect Dis

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Purpose of Review Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene. Recent Findings AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed. Summary Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

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“…3 CNNs have recently led to significant breakthroughs in the analysis of biomedical images, such as diagnosis of skin tumors, retinal disease, intracranial hemorrhage, and breast cancer. [3][4][5][6][7][8] In the context of routine hematoxylin and eosin (H&E) tissue stains, similar algorithms have improved Gleason scoring in prostate cancer, outcome prediction in colorectal cancer, and even discrimination of solid cancer patients by driver mutation status. 5,[9][10][11] Here, we investigate the potential of CNN-based morphological analysis in hematology.…”

Section: Introductionmentioning

confidence: 99%

Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

Brück

Lallukka-Brück

Hohtari

et al. 2020

Preprint

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In myelodysplastic syndrome (MDS), bone marrow (BM) histopathology is visually assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, many morphological findings elude the human eye. Here, we extracted visual features of 236 MDS, 87 MDS/MPN, and 10 control BM biopsies with convolutional neural networks. Unsupervised analysis distinguished underlying correlations between tissue composition, leukocyte metrics, and clinical characteristics. We applied morphological features in elastic net-regularized regression models to predict genetic and cytogenetic aberrations, prognosis, and clinical variables. By parallelizing tile, pixel, and leukocytelevel image analysis, we deconvoluted each model to texture and cellular composition to dissect their pathobiological context. Model-based mutation predictions correlated with variant allele frequency and number of affected genes per pathway, demonstrating the models' ability to identify relevant visual patterns. In summary, this study highlights the potential of deep histopathology in hematology by unveiling the fundamental association of BM morphology with genetic and clinical determinants.

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International evaluation of an AI system for breast cancer screening

Cited by 1,806 publications

References 57 publications

Evaluating AI in breast cancer screening: a complex task

Evaluating AI in breast cancer screening: a complex task

Using Artificial Intelligence in Infection Prevention

Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

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