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- Puri, Rishi, et al.
(författare)
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Transcatheter aortic valve implantation in patients with small aortic annuli using a 20 mm balloon-expanding valve
- 2017
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Ingår i: Heart. - : BMJ. - 1355-6037 .- 1468-201X. ; 103, s. 148-153
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Tidskriftsartikel (refereegranskat)abstract
- Background While transcatheter aortic valve implantation (TAVI) is established for treating highoperative risk surgical aortic valve replacement candidates, until recently the smallest transcatheter heart valve (THV) measured 23 mm, posing greater risk for annular rupture and THV failure in patients with aortic stenosis (AS) with small aortic annuli (=20 mm). Objectives In the setting of a multicentre registry, we report on the safety, efficacy and early clinical outcomes of the SAPIEN XT 20 mm balloon-expanding THV. Results Among TAVI 55 recipients (n=30 for native AS, n=25 for a valve-in-valve procedure (V-in-V)), median age and Society of Thoracic Surgeons score were 85 (81 to 87) years and 7.8 (4.7 to 12.4)%, respectively. Mean and minimum annular diameters were 19±1 and 17±2 mm, respectively, in native patients with AS, and 17±1 mm (internal diameter) in V-in-V recipients. Successful device implantation rate was 96%, with no procedural-related death. Overall in-hospital-30-day death, stroke and major bleeding rates were 5%, 2% and 9%, respectively. In native AS TAVI recipients, mean transaortic gradient decreased from 54±20 to 12±5 mm Hg ( p<0.001), and from 45±17 to 24±8 mm Hg (p<0.001) in V-in-V recipients. Severe prosthesis-patient mismatch (PPM) rates were 10% and 48% in native AS and V-in-V TAVI recipients, respectively ( p=0.03). Post-TAVI, the rate of moderate aortic regurgitation was 7% and 0% in native AS and V-in-V TAVI recipients, respectively. Conclusions TAVI with the 20 mm SAPIEN XT THV appears safe and technically feasible, with acceptable short-term clinical outcomes and low rates of severe PPM in those with native AS.
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| 2. |
- Yang, Ariel, et al.
(författare)
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Kankanet : An artificial neural network-based object detection smartphone application and mobile microscope as a point-of-care diagnostic aid for soil-transmitted helminthiases
- 2019
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Ingår i: PLoS Neglected Tropical Diseases. - : Public Library of Science (PLoS). - 1935-2727 .- 1935-2735. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundEndemic areas for soil-transmitted helminthiases often lack the tools and trained personnel necessary for point-of-care diagnosis. This study pilots the use of smartphone microscopy and an artificial neural network-based (ANN) object detection application named Kankanet to address those two needs.Methodology/Principal findingsA smartphone was equipped with a USB Video Class (UVC) microscope attachment and Kankanet, which was trained to recognize eggs of Ascaris lumbricoides, Trichuris trichiura, and hookworm using a dataset of 2,078 images. It was evaluated for interpretive accuracy based on 185 new images. Fecal samples were processed using Kato-Katz (KK), spontaneous sedimentation technique in tube (SSTT), and Merthiolate-Iodine-Formaldehyde (MIF) techniques. UVC imaging and ANN interpretation of these slides was compared to parasitologist interpretation of standard microscopy.Relative to a gold standard defined as any positive result from parasitologist reading of KK, SSTT, and MIF preparations through standard microscopy, parasitologists reading UVC imaging of SSTT achieved a comparable sensitivity (82.9%) and specificity (97.1%) in A. lumbricoides to standard KK interpretation (97.0% sensitivity, 96.0% specificity). The UVC could not accurately image T. trichiura or hookworm. Though Kankanet interpretation was not quite as sensitive as parasitologist interpretation, it still achieved high sensitivity for A. lumbricoides and hookworm (69.6% and 71.4%, respectively). Kankanet showed high sensitivity for T. trichiura in microscope images (100.0%), but low in UVC images (50.0%).Conclusions/SignificanceThe UVC achieved comparable sensitivity to standard microscopy with only A. lumbricoides. With further improvement of image resolution and magnification, UVC shows promise as a point-of-care imaging tool. In addition to smartphone microscopy, ANN-based object detection can be developed as a diagnostic aid. Though trained with a limited dataset, Kankanet accurately interprets both standard microscope and low-quality UVC images. Kankanet may achieve sensitivity comparable to parasitologists with continued expansion of the image database and improvement of machine learning technology.
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