Michal Meiri

AI
Operational applications drive AI adoption in radiology
When talking about artificial intelligence (AI) in radiology, one immediately thinks of the classic clinical use case of computer vision and powerful algorithms identifying abnormalities on imaging studies. Operational applications of AI, however, will likely be the first to be widely adopted.
March 16, 2020
2018 08 28 18 59 9066 Artificial Intelligence Ai 400
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(A) Overview of study participants with different clinical phenotypes depicted in distinct colors: Limbic encephalitis (LE)/temporal lobe epilepsy (TLE): Blue; cerebellar ataxia (CA): Green; stiff person syndrome (SPS): Red, including overlapping clinical features with an overview of the analyzed parameter; generated in Biorender. (B) F-18 F-DED uptake, as visualized by SUVr with global mean scaling, is shown for three representative patients with GAD65-AIE: Patient #6 with LE/TLE, showing mesiotemporal F-18 F-DED uptake; patient #16 with overlapping clinical features of CA, SPS, and LE, showing both mesiotemporal and cerebellar F-18 F-DED uptake; and patient #17 with isolated SPS showing no increased F-18 F-DED uptake. Additionally, the average F-18 F-DED uptake of eight controls is shown on the right.
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Representative images of participants with difficulty with breath holding and arrhythmia. In the lower right corner of A–L, the position coordinates of the images are marked. Short-axis (SA) view images in the end-systole (A–F) and end-diastole (G–L) phases in participants with atrial fibrillation. (A–C, G–I) Balanced steady-state free precession (bSSFP) cardiac cine. (D–F, J–L) Deep learning–enhanced compressed sensing (AI-CS) single-shot cardiac cine. In contrast to AI-CS single-shot cardiac cine, endocardial contours were challenging to identify on conventional segmented cine in midventricular and apical sections. (M) Line graph of time curve during the whole cardiac cycle obtained from bSSFP cardiac cine. (N) Line graph of time curve during the whole cardiac cycle obtained from AI-CS single-shot cardiac cine. LV = left ventricle; 2D = two-dimensional.
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(A) Overview of study participants with different clinical phenotypes depicted in distinct colors: Limbic encephalitis (LE)/temporal lobe epilepsy (TLE): Blue; cerebellar ataxia (CA): Green; stiff person syndrome (SPS): Red, including overlapping clinical features with an overview of the analyzed parameter; generated in Biorender. (B) F-18 F-DED uptake, as visualized by SUVr with global mean scaling, is shown for three representative patients with GAD65-AIE: Patient #6 with LE/TLE, showing mesiotemporal F-18 F-DED uptake; patient #16 with overlapping clinical features of CA, SPS, and LE, showing both mesiotemporal and cerebellar F-18 F-DED uptake; and patient #17 with isolated SPS showing no increased F-18 F-DED uptake. Additionally, the average F-18 F-DED uptake of eight controls is shown on the right.
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Representative images of participants with difficulty with breath holding and arrhythmia. In the lower right corner of A–L, the position coordinates of the images are marked. Short-axis (SA) view images in the end-systole (A–F) and end-diastole (G–L) phases in participants with atrial fibrillation. (A–C, G–I) Balanced steady-state free precession (bSSFP) cardiac cine. (D–F, J–L) Deep learning–enhanced compressed sensing (AI-CS) single-shot cardiac cine. In contrast to AI-CS single-shot cardiac cine, endocardial contours were challenging to identify on conventional segmented cine in midventricular and apical sections. (M) Line graph of time curve during the whole cardiac cycle obtained from bSSFP cardiac cine. (N) Line graph of time curve during the whole cardiac cycle obtained from AI-CS single-shot cardiac cine. LV = left ventricle; 2D = two-dimensional.
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