Dr. Christopher Austin

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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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Example of AI-assisted pulmonary nodule detection and longitudinal volumetric assessment in a 65-year-old woman with peripheral arterial disease, a 24-pack-year smoking history, emphysema, and a family history of lung cancer. An incidental 8.5-mm right lower lobe pulmonary nodule is identified at CT angiography of the abdomen in July 2022 but does not receive follow-up. Subsequent imaging demonstrates interval growth, and CT-guided biopsy confirms stage IA lung adenocarcinoma. Contrast-enhanced axial chest CT images obtained on February 2, 2024, before robotic-assisted thoracoscopic segmentectomy, show a 12.1-mm spiculated right lower lobe pulmonary nodule. (A) Conventional contrast-enhanced axial chest CT image without AI assistance shows a 12.1-mm spiculated right lower lobe pulmonary nodule. (B) Corresponding CT image with AI-assisted pulmonary nodule detection, automated segmentation, and quantitative nodule assessment identifies a solid pulmonary nodule (nodule #1) measuring 13 × 8 (11) mm, with a volume of 739 mm3, a 45% volumetric increase compared with the previous contrast-enhanced chest CT performed on October 9, 2023 (510 mm3), and an estimated volume doubling time (VDT) of 216 days. (C) AI-generated longitudinal nodule analysis displays automated segmentation of the current (February 2, 2024) and previous (October 9, 2023) CT examinations, 3D nodule reconstruction, and quantitative volumetric assessment for longitudinal follow-up. The AI interface is displayed in the original software language (Dutch).
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