Ross Upton, PhD

Artificial Intelligence
Can AI give radiology the urgent jump-start it needs?
In the past, healthcare has adopted new technology at a similar pace to a supertanker. Now that the healthcare supertanker has been driven into the eye of the COVID-19 storm, there's a strong need to embrace artificial intelligence (AI) and new ways of working, writes Ross Upton, PhD.
August 5, 2020
2017 05 09 11 15 29 355 Cyber Informatics 400
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Cardiac MRI analysis can provide useful information on epicardial adipose tissue, infarct size, microvascular obstruction, and myocardial edema.
Cardiovascular Radiology
MRI casts light on patient outcome after heart attack
Increased volume of fat tissue around the heart tends can be associated with greater myocardial damage after a heart attack, German researchers have reported.
A smartphone is used to take an image for a 3D-printed prosthesis.
Advanced Visualization
3D-printed prostheses bring hope in conflict zones
Obesity Text2
MRI
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CT
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Example case from test set with biparametric MRI data, reference segmentation, and output from AI system. The primary MRI assessment resulted in a PI-RADS 5 lesion in the left dorsal part of the peripheral zone and a PI-RADS 4 lesion in the right dorsal part of the peripheral zone (PSA of 11.6 μg/L). Targeted biopsies from both areas showed ISUP-grade 3. All images are from the same slice location, showing a part of both lesions. A T2-weighted (T2W) image. B Diffusion-weighted image with a b-value of 1,500 s/mm2. (C) Apparent diffusion coefficient map. (D) Reference segmentation with white areas representing tumor. (E) Softmax output from AI system. Inset in the lower left corner shows the detection map (cropped around the prostate) with red voxels representing softmax values of 0.99 and yellow voxels representing softmax values of 0.59. (F) T2W image with reference (D) and detection map (inset in E) overlayed, green represents areas in which both the reference and the AI output are positive (true positive voxels for AI), red only positive in reference (false-negative voxels for AI) and blue only segmented by AI (false-positive voxels for AI).
MRI
AI comes under scrutiny in Swedish prostate MRI study
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AI comes under scrutiny in Swedish prostate MRI study
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An AI system trained in a screening population has lower specificity than radiologists using PI-RADS v2, but considerable care is vital, researchers from Gothenburg reported in European Radiology on 8 December.
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Example case from test set with biparametric MRI data, reference segmentation, and output from AI system. The primary MRI assessment resulted in a PI-RADS 5 lesion in the left dorsal part of the peripheral zone and a PI-RADS 4 lesion in the right dorsal part of the peripheral zone (PSA of 11.6 μg/L). Targeted biopsies from both areas showed ISUP-grade 3. All images are from the same slice location, showing a part of both lesions. A T2-weighted (T2W) image. B Diffusion-weighted image with a b-value of 1,500 s/mm2. (C) Apparent diffusion coefficient map. (D) Reference segmentation with white areas representing tumor. (E) Softmax output from AI system. Inset in the lower left corner shows the detection map (cropped around the prostate) with red voxels representing softmax values of 0.99 and yellow voxels representing softmax values of 0.59. (F) T2W image with reference (D) and detection map (inset in E) overlayed, green represents areas in which both the reference and the AI output are positive (true positive voxels for AI), red only positive in reference (false-negative voxels for AI) and blue only segmented by AI (false-positive voxels for AI).
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