
Chinese General Practice ›› 2023, Vol. 26 ›› Issue (15): 1847-1856.DOI: 10.12114/j.issn.1007-9572.2022.0851
Special Issue: 内分泌代谢性疾病最新文章合辑; 数智医疗最新文章合辑
• Original Research·Diabetes Complications • Previous Articles Next Articles
Received:2022-12-16
Revised:2023-01-24
Published:2023-05-20
Online:2022-12-20
Contact:
DUAN Junguo
通讯作者:
段俊国
作者简介:基金资助:
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URL: https://www.chinagp.net/EN/10.12114/j.issn.1007-9572.2022.0851
| 序号 | 被引作者 | 被引频次(次) | 中介中心性 |
|---|---|---|---|
| 1 | GULSHAN V | 412 | 0.03 |
| 2 | ABRÀMOFF M D | 363 | 0.17 |
| 3 | TING D W | 285 | 0.04 |
| 4 | NIEMEIJER M | 240 | 0.02 |
| 5 | HE K | 201 | 0.04 |
| 6 | QUELLEC G | 179 | 0.04 |
| 7 | GARGEYA R | 176 | 0.02 |
| 8 | SZEGEDY C | 172 | 0.03 |
| 9 | DECENCIERE E | 164 | 0.04 |
| 10 | KRIZHEVSKY A | 153 | 0.03 |
Table 1 The top 10 authors of cited frequency for studies regarding artificial intelligence in diabetic retinopathy
| 序号 | 被引作者 | 被引频次(次) | 中介中心性 |
|---|---|---|---|
| 1 | GULSHAN V | 412 | 0.03 |
| 2 | ABRÀMOFF M D | 363 | 0.17 |
| 3 | TING D W | 285 | 0.04 |
| 4 | NIEMEIJER M | 240 | 0.02 |
| 5 | HE K | 201 | 0.04 |
| 6 | QUELLEC G | 179 | 0.04 |
| 7 | GARGEYA R | 176 | 0.02 |
| 8 | SZEGEDY C | 172 | 0.03 |
| 9 | DECENCIERE E | 164 | 0.04 |
| 10 | KRIZHEVSKY A | 153 | 0.03 |
| 序号 | 被引期刊 | JCR分区 | IF | 被引频次 | 中介中心性 |
|---|---|---|---|---|---|
| 1 | Ophthalmology | Q1 | 14.28 | 791 | 0.02 |
| 2 | Invest Ophth Vis Sci | Q1 | 4.93 | 711 | 0.01 |
| 3 | Ieee T Med Imaging | Q1 | 11.04 | 628 | 0.01 |
| 4 | Brit J Ophthalmol | Q1 | 5.91 | 576 | 0.01 |
| 5 | Jama-J Am Med Assoc | Q1 | 157.38 | 526 | 0.03 |
| 6 | Med Image Anal | Q1 | 13.83 | 432 | 0.01 |
| 7 | Diabetes Care | Q1 | 17.16 | 431 | 0.01 |
| 8 | Plos One | Q2 | 3.75 | 413 | 0.02 |
| 9 | Am J Ophthalmol | Q1 | 5.49 | 403 | 0.01 |
| 10 | Lect Notes Comput Sc | 未查到 | — | 391 | 0.03 |
Table 2 The top 10 journals of cited frequency for studies regarding artificial intelligence in diabetic retinopathy
| 序号 | 被引期刊 | JCR分区 | IF | 被引频次 | 中介中心性 |
|---|---|---|---|---|---|
| 1 | Ophthalmology | Q1 | 14.28 | 791 | 0.02 |
| 2 | Invest Ophth Vis Sci | Q1 | 4.93 | 711 | 0.01 |
| 3 | Ieee T Med Imaging | Q1 | 11.04 | 628 | 0.01 |
| 4 | Brit J Ophthalmol | Q1 | 5.91 | 576 | 0.01 |
| 5 | Jama-J Am Med Assoc | Q1 | 157.38 | 526 | 0.03 |
| 6 | Med Image Anal | Q1 | 13.83 | 432 | 0.01 |
| 7 | Diabetes Care | Q1 | 17.16 | 431 | 0.01 |
| 8 | Plos One | Q2 | 3.75 | 413 | 0.02 |
| 9 | Am J Ophthalmol | Q1 | 5.49 | 403 | 0.01 |
| 10 | Lect Notes Comput Sc | 未查到 | — | 391 | 0.03 |
| 序号 | 文章标题 | 第一作者 | 发表年份(年) | 共被引频次(次) |
|---|---|---|---|---|
| 1 | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[ | GULSHAN V | 2016 | 354 |
| 2 | Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes[ | TING D W | 2017 | 218 |
| 3 | Automated Identification of Diabetic Retinopathy Using Deep Learning[ | GARGEYA R | 2017 | 175 |
| 4 | Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning[ | ABRÀMOFF M D | 2016 | 127 |
| 5 | Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[ | ABRÀMOFF M D | 2018 | 74 |
| 6 | Deep learning[ | LECUN Y | 2015 | 69 |
| 7 | Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning[ | KERMANY D S | 2018 | 61 |
| 8 | Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[ | POPLIN R | 2018 | 61 |
| 9 | Clinically applicable deep learning for diagnosis and referral in retinal disease[ | DE F | 2018 | 59 |
| 10 | Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy[ | KRAUSE J | 2018 | 58 |
Table 3 The top 10 most-cited articles for studies regarding artificial intelligence in diabetic retinopathy
| 序号 | 文章标题 | 第一作者 | 发表年份(年) | 共被引频次(次) |
|---|---|---|---|---|
| 1 | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[ | GULSHAN V | 2016 | 354 |
| 2 | Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes[ | TING D W | 2017 | 218 |
| 3 | Automated Identification of Diabetic Retinopathy Using Deep Learning[ | GARGEYA R | 2017 | 175 |
| 4 | Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning[ | ABRÀMOFF M D | 2016 | 127 |
| 5 | Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[ | ABRÀMOFF M D | 2018 | 74 |
| 6 | Deep learning[ | LECUN Y | 2015 | 69 |
| 7 | Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning[ | KERMANY D S | 2018 | 61 |
| 8 | Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[ | POPLIN R | 2018 | 61 |
| 9 | Clinically applicable deep learning for diagnosis and referral in retinal disease[ | DE F | 2018 | 59 |
| 10 | Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy[ | KRAUSE J | 2018 | 58 |
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