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1.
Ophthalmology ; 129(5): 571-584, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-34990643

RESUMO

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.


Assuntos
Extração de Catarata , Catarata , Aprendizado Profundo , Catarata/diagnóstico , Humanos , Fotografação
2.
Org Biomol Chem ; 18(17): 3281-3287, 2020 05 06.
Artigo em Inglês | MEDLINE | ID: mdl-32319502

RESUMO

O-Cyclopropyl hydroxylamines, now accessible via a novel and scalable synthetic route, have been demonstrated to be bench-stable and practical precursors for the synthesis of N-heterocycles via a di-heteroatom [3,3]-sigmatropic rearrangement. In order to study the reactivity of these compounds in depth, a robust synthesis of both ring-substituted and ring-unsubstituted O-cyclopropyl hydroxylamines has been developed. Metal-free conditions for the facile N-arylation of these precursors were also identified. It was found that the N-arylated O-cyclopropyl hydroxamates can efficiently undergo a one-pot [3,3]-sigmatropic rearrangement/cyclization/rearomatization cascade under base-mediated conditions to furnish a structurally diverse set of substituted tetrahydroquinolines.


Assuntos
Compostos Heterocíclicos/química , Hidroxilaminas/síntese química , Ciclização , Hidroxilamina/química , Estrutura Molecular , Quinolinas/química , Estereoisomerismo
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