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A web-based tool for real-time adequacy assessment of kidney biopsies.
Ahangaran, Meysam; Sun, Emily; Le, Khang; Sun, Jiawei; Wang, William M; Tan, Tian Herng; Burdine, Lyle J; Dvanajscak, Zeljko; Cassol, Clarissa A; Sharma, Shree; Kolachalama, Vijaya B.
Afiliación
  • Ahangaran M; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Sun E; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Le K; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Sun J; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Wang WM; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Tan TH; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
  • Burdine LJ; Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, US.
  • Dvanajscak Z; Arkana Laboratories, Little Rock, AR, USA.
  • Cassol CA; Arkana Laboratories, Little Rock, AR, USA.
  • Sharma S; Arkana Laboratories, Little Rock, AR, USA.
  • Kolachalama VB; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
medRxiv ; 2024 Feb 05.
Article en En | MEDLINE | ID: mdl-38370740
ABSTRACT
The escalating incidence of kidney biopsies providing insufficient tissue for diagnosis poses a dual challenge, straining the healthcare system and jeopardizing patients who may require rebiopsy or face the prospect of an inaccurate diagnosis due to an unsampled disease. Here, we introduce a web-based tool that can provide real-time, quantitative assessment of kidney biopsy adequacy directly from photographs taken with a smartphone camera. The software tool was developed using a deep learning-driven automated segmentation technique, trained on a dataset comprising nephropathologist-confirmed annotations of the kidney cortex on digital biopsy images. Our framework demonstrated favorable performance in segmenting the cortex via 5-fold cross-validation (Dice coefficient 0.788±0.130) (n=100). Offering a bedside tool for kidney biopsy adequacy assessment has the potential to provide real-time guidance to the physicians performing medical kidney biopsies, reducing the necessity for re-biopsies. Our tool can be accessed through our web-based platform http//www.biopsyadequacy.org.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
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