Dr. Christopher Austin

IS
3 questions to shape the future of AI and radiology
The introduction of disruptive healthcare technologies rarely threatens the role of the clinician -- but radiology is fast becoming the exception. In this column, Dr. Christopher Austin of GE Healthcare suggests three questions that radiologists should ask themselves if they want to lead and not follow the changes that are coming.
November 15, 2016
2016 11 14 12 36 15 302 Austin Christopher 400
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CT
New benchmark helps monitor cumulative CT radiation dose
The findings highlight the need for systematic dose tracking and rigorous justification of imaging.
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Radiation Oncology/Therapy
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Findings suggest a possible hazard associated with postoperative radiotherapy.
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CEUS outperformed color Doppler ultrasound in a study focusing on indeterminate kidney lesions.
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Vilert Loving, MD, from Ochsner Health and the SBI discusses breast cancer screening based on annual mammography versus risk.
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Could AI scoring help with managing DCIS?
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An AI model showed associations with recurrence after DCIS surgery and performed comparably to established clinical risk models.
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Images of a 39-year-old who underwent screening mammography. Right craniocaudal (left) and mediolateral oblique (middle) views from preoperative mammography show suspicious grouped fine pleomorphic calcifications (arrows). After biopsy showed ductal carcinoma of situ (DCIS), patient underwent breast-conserving surgery, yielding final diagnosis of ductal carcinoma in situ (intermediate grade, hormone receptor positive). (Right) Screenshot of an output of a commercial AI tool applied to preoperative mammography. AI detected right breast lesion with score of 95% on both views (concentric colored circles). Present study’s reviewing radiologist deemed AI marking to correspond with site of DCIS.
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Images from two representative cases. (A-C) Images in an 82-year-old man with a calcified plaque in the proximal segment of the left anterior descending artery. (A, B) Curved planar reconstruction images from CCTA with (A) hybrid iterative reconstruction (HIR) and (B) super-resolution deep learning reconstruction (SR-DLR) show that the measured PDS was 66% for the HIR image and 42% for the SR-DLR image. (C) Quantitative analysis of the invasive coronary angiography (ICA) image (reference standard) revealed a PDS of 34% (arrow). (D-F) Images in a 37-year-old woman with a noncalcified plaque in the middle segment of the left circumflex artery. (D, E) Curved planar reconstruction images from CCTA with (D) HIR and (E) SR-DLR show that the measured PDS was 47% for the HIR image and 80% for the SR-DLR image. (F) Quantitative analysis of the ICA image revealed a PDS of 72% (arrow). Blue, red, and yellow lines in A, B, D, and E represent the proximal reference point, stenosed lesion, and distal reference point, respectively, and the insets show the corresponding cross-sectional images.
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"Most participants undergoing HCC screening had excellent MRI quality even when ultrasound was limited," researchers noted.
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