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Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy.
De Vera Mudry, Maria Cristina; Martin, Jim; Schumacher, Vanessa; Venugopal, Raghavan.
Afiliação
  • De Vera Mudry MC; Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, 1529F. Hoffmann-La Roche Ltd, Basel, Switzerland.
  • Martin J; 1529Roche Tissue Diagnostics, Santa Clara, CA, USA.
  • Schumacher V; Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, 1529F. Hoffmann-La Roche Ltd, Basel, Switzerland.
  • Venugopal R; 1529Roche Tissue Diagnostics, Santa Clara, CA, USA.
Toxicol Pathol ; 49(4): 851-861, 2021 06.
Article em En | MEDLINE | ID: mdl-33371793
ABSTRACT
Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal atrophy, particularly of the outer and inner nuclear layers, in rats. Herein, we report our experience creating and employing a hybrid approach, which combines conventional image processing and DL to quantify rodent retinal atrophy. Utilizing a DL approach based upon the VGG16 model architecture, models were trained, tested, and validated using 10,746 image patches scanned from whole slide images (WSIs) of hematoxylin-eosin stained rodent retina. The accuracy of this computational method was validated using pathologist annotated WSIs throughout and used to separately quantify the thickness of the outer and inner nuclear layers of the retina. Our results show that DL can facilitate the evaluation of therapeutic and/or light-induced atrophy, particularly of the outer retina, efficiently in rodents. In addition, this study provides a template which can be used to train, validate, and analyze the results of toxicologic pathology DL models across different animal species used in preclinical efficacy and safety studies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Degeneração Retiniana / Aprendizado Profundo Tipo de estudo: Guideline Limite: Animals Idioma: En Revista: Toxicol Pathol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Degeneração Retiniana / Aprendizado Profundo Tipo de estudo: Guideline Limite: Animals Idioma: En Revista: Toxicol Pathol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça