Table 1.
Top publications in the ranking of annual citation count and total citation count.
| References | Title | Year | C/Y | Source Title |
|---|---|---|---|---|
| Arita et al. [20] | Objective image analysis of the meibomian gland area | 2014 | 7.36 | Br J Ophthalmol |
| Song et al. [10] | A clinical decision model based on machine learning for ptosis | 2021 | 5.5 | BMC Ophthalmol |
| Xiao et al. [21] | An automated and multiparametric algorithm for objective analysis of meibography images | 2021 | 4.75 | Quant Imaging Med Surg |
| Wang et al. [22] | A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images | 2019 | 4.67 | Transl Vis Sci Technol |
| Koh et al. [19] | Detection of meibomian glands and classification of meibography images | 2012 | 4.38 | J Biomed Opt |
| Maruoka et al. [23] | Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy | 2020 | 4.2 | Cornea |
| Prabhu et al. [24] | Deep learning segmentation and quantification of Meibomian glands | 2020 | 4.2 | Biomed Signal Process Control |
| Llorens-Quintana et al. [25] | A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology | 2019 | 3.67 | Transl Vis Sci Technol |
| Zhang et al. [26] | Meibomian Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning | 2022 | 3.67 | J Clin Med |
| Li et al. [27] |
Artificial intelligence to detect malignant eyelid tumors from photographic images |
2022 |
3.67 |
NPJ Digit Med |
|
References |
Title |
Year |
TC |
Source Title |
| Arita et al. [20] | Objective image analysis of the meibomian gland area | 2014 | 81 | Br J Ophthalmol |
| Koh et al. [19] | Detection of meibomian glands and classification of meibography images | 2012 | 57 | J Biomed Opt |
| Wang et al. [22] | A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images | 2019 | 28 | Transl Vis Sci Technol |
| Koprowski et al. [28] | A quantitative method for assessing the quality of meibomian glands | 2016 | 25 | Comput Biol Med |
| Bodnar et al. [29] | Automated Ptosis Measurements From Facial Photographs | 2016 | 23 | JAMA Ophthalmol |
| Song et al. [10] | A clinical decision model based on machine learning for ptosis | 2021 | 22 | BMC Ophthalmol |
| Llorens-Quintana et al. [25] | A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology | 2019 | 22 | Transl Vis Sci Technol |
| Maruoka et al. [23] | Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy | 2020 | 21 | Cornea |
| Prabhu et al. [24] | Deep learning segmentation and quantification of Meibomian glands | 2020 | 21 | Biomed Signal Process Control |
| Koprowski et al. [30] | A clinical utility assessment of the automatic measurement method of the quality of Meibomian glands | 2017 | 21 | Biomed Eng Online |
C/Y: average citation count per year; TC: total citation count.