Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy
Abstract Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, microneuromas (aberrant growth and swelling of the corn...
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01577-3 |
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| author | Neslihan Dilruba Koseoglu Eric Chen Rudraksh Tuwani Benjamin Kompa Stephanie M. Cox M. Cuneyt Ozmen Mina Massaro-Giordano Andrew L. Beam Pedram Hamrah |
| author_facet | Neslihan Dilruba Koseoglu Eric Chen Rudraksh Tuwani Benjamin Kompa Stephanie M. Cox M. Cuneyt Ozmen Mina Massaro-Giordano Andrew L. Beam Pedram Hamrah |
| author_sort | Neslihan Dilruba Koseoglu |
| collection | DOAJ |
| description | Abstract Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, microneuromas (aberrant growth and swelling of the corneal nerve endings) detected using in vivo confocal microscopy (IVCM) have emerged as a promising biomarker for NCP. However, this process is time-intensive and error-prone, limiting its clinical use and availability. In this work, we present a new NCP screening system based on a deep learning model trained to detect microneuromas using a multisite dataset with a combined total of 103,168 IVCM images. Our model showed excellent discriminative ability detecting microneuromas (AuROC: 0.97) and the ability to generalize to data from a new institution (AuROC: 0.90). Additionally, our pipeline provides an uncertainty quantification mechanism that allows it to communicate when its predictions are reliable, further increasing its clinical relevance. |
| format | Article |
| id | doaj-art-ff62926e0aba492e911f742483f8c7d0 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-ff62926e0aba492e911f742483f8c7d02025-08-20T01:51:31ZengNature Portfolionpj Digital Medicine2398-63522025-05-01811810.1038/s41746-025-01577-3Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopyNeslihan Dilruba Koseoglu0Eric Chen1Rudraksh Tuwani2Benjamin Kompa3Stephanie M. Cox4M. Cuneyt Ozmen5Mina Massaro-Giordano6Andrew L. Beam7Pedram Hamrah8Center for Translational Ocular Immunology and Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of MedicineHarvard-MIT Program in Health Sciences and TechnologyDepartment of Epidemiology, Harvard T.H. Chan School of Public HealthDepartment of Biomedical Informatics, Harvard Medical SchoolCenter for Translational Ocular Immunology and Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of MedicineCenter for Translational Ocular Immunology and Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of MedicineScheie Eye Institute, Department of Ophthalmology, University of PennsylvaniaDepartment of Epidemiology, Harvard T.H. Chan School of Public HealthCenter for Translational Ocular Immunology and Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of MedicineAbstract Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resulting in chronic pain and discomfort even in the absence of noxious stimuli. Recently, microneuromas (aberrant growth and swelling of the corneal nerve endings) detected using in vivo confocal microscopy (IVCM) have emerged as a promising biomarker for NCP. However, this process is time-intensive and error-prone, limiting its clinical use and availability. In this work, we present a new NCP screening system based on a deep learning model trained to detect microneuromas using a multisite dataset with a combined total of 103,168 IVCM images. Our model showed excellent discriminative ability detecting microneuromas (AuROC: 0.97) and the ability to generalize to data from a new institution (AuROC: 0.90). Additionally, our pipeline provides an uncertainty quantification mechanism that allows it to communicate when its predictions are reliable, further increasing its clinical relevance.https://doi.org/10.1038/s41746-025-01577-3 |
| spellingShingle | Neslihan Dilruba Koseoglu Eric Chen Rudraksh Tuwani Benjamin Kompa Stephanie M. Cox M. Cuneyt Ozmen Mina Massaro-Giordano Andrew L. Beam Pedram Hamrah Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy npj Digital Medicine |
| title | Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| title_full | Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| title_fullStr | Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| title_full_unstemmed | Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| title_short | Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| title_sort | development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy |
| url | https://doi.org/10.1038/s41746-025-01577-3 |
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