Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model
Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous efforts to identify early-stage glaucoma have faced challenges, including insufficient accuracy, sensitivity, and specificity. This study presents a...
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MDPI AG
2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/5/516 |
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| author | Wheyming Song Ing-Chou Lai |
| author_facet | Wheyming Song Ing-Chou Lai |
| author_sort | Wheyming Song |
| collection | DOAJ |
| description | Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous efforts to identify early-stage glaucoma have faced challenges, including insufficient accuracy, sensitivity, and specificity. This study presents a concatenated artificial intelligence model that combines two types of input features: fundus images and quantitative retinal thickness parameters derived from macular and peri-papillary retinal nerve fiber layer (RNFL) thickness measurements. These features undergo an intelligent transformation, referred to as “smart preprocessing”, to enhance their utility. The model employs two classification approaches: a convolutional neural network approach for processing image features and an artificial neural network approach for analyzing quantitative retinal thickness parameters. To maximize performance, hyperparameters were fine-tuned using a robust methodology for the design of experiments. The proposed AI model demonstrated outstanding performance in early-stage glaucoma detection, outperforming existing models; its accuracy, sensitivity, specificity, precision, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>-Score all exceeding 0.90. |
| format | Article |
| id | doaj-art-903a3176a9f4482fbf41dc280a53478a |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-903a3176a9f4482fbf41dc280a53478a2025-08-20T01:56:25ZengMDPI AGBioengineering2306-53542025-05-0112551610.3390/bioengineering12050516Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI ModelWheyming Song0Ing-Chou Lai1Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407102, TaiwanDepartment of Ophthalmology, Chiayi Chang Gung Memorial Hospital, Puzi City 61363, TaiwanGlaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous efforts to identify early-stage glaucoma have faced challenges, including insufficient accuracy, sensitivity, and specificity. This study presents a concatenated artificial intelligence model that combines two types of input features: fundus images and quantitative retinal thickness parameters derived from macular and peri-papillary retinal nerve fiber layer (RNFL) thickness measurements. These features undergo an intelligent transformation, referred to as “smart preprocessing”, to enhance their utility. The model employs two classification approaches: a convolutional neural network approach for processing image features and an artificial neural network approach for analyzing quantitative retinal thickness parameters. To maximize performance, hyperparameters were fine-tuned using a robust methodology for the design of experiments. The proposed AI model demonstrated outstanding performance in early-stage glaucoma detection, outperforming existing models; its accuracy, sensitivity, specificity, precision, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula>-Score all exceeding 0.90.https://www.mdpi.com/2306-5354/12/5/516glaucomairreversible blindnessperipapillary retinal nerve fiber layer thicknessmacular thickness |
| spellingShingle | Wheyming Song Ing-Chou Lai Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model Bioengineering glaucoma irreversible blindness peripapillary retinal nerve fiber layer thickness macular thickness |
| title | Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model |
| title_full | Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model |
| title_fullStr | Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model |
| title_full_unstemmed | Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model |
| title_short | Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model |
| title_sort | automated early stage glaucoma detection using a robust concatenated ai model |
| topic | glaucoma irreversible blindness peripapillary retinal nerve fiber layer thickness macular thickness |
| url | https://www.mdpi.com/2306-5354/12/5/516 |
| work_keys_str_mv | AT wheymingsong automatedearlystageglaucomadetectionusingarobustconcatenatedaimodel AT ingchoulai automatedearlystageglaucomadetectionusingarobustconcatenatedaimodel |