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Kim AD, Solomon AL, Ratchford EV. Vascular Disease Patient Information Page: Popliteal artery aneurysm. Vasc Med 2024; 29:357-361. [PMID: 38573080 DOI: 10.1177/1358863x241241019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Affiliation(s)
- Andrea D Kim
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Elizabeth V Ratchford
- Johns Hopkins Center for Vascular Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Bellomo TR, Goudot G, Lella SK, Landau E, Sumetsky N, Zacharias N, Fischetti C, Dua A. Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images. Diagnostics (Basel) 2023; 14:46. [PMID: 38201355 PMCID: PMC10795888 DOI: 10.3390/diagnostics14010046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/16/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.
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Affiliation(s)
- Tiffany R. Bellomo
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
- Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Guillaume Goudot
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
| | - Srihari K. Lella
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
- Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Eric Landau
- Encord, Cord Technologies Inc., New York City, NY 10013, USA;
| | - Natalie Sumetsky
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
| | - Nikolaos Zacharias
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
- Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Chanel Fischetti
- Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Anahita Dua
- Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; (G.G.); (S.K.L.); (N.S.); (N.Z.); (A.D.)
- Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA;
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