Cataracts and retinal disease (such as age-related macular degeneration, diabetic retinopathy, macular oedema etc.) are the leading causes of blindness worldwide, whose prevalence increases with age. More specifically, the prevalence of cataracts was 3.9% among 55–64 years to 92.6% among those 80 years and older; the prevalence of retinal disease was in the range of 51.26% to 53.05% for ages more than 60 years.
Refractive cataract surgery aims to remove cataracts while treating refractive errors using premium or advanced intraocular lenses to restore optimal vision and reduce the patient’s dependence on glasses. However, coexisting retinal disorders increase the incidence with risks of surgical complications and make the surgery more challenging. Therefore, optimizing preoperative, intraoperative, and postoperative management strategies could improve patients’ visual prognosis.
With the increasing dependence of clinicians on medical imaging in the ophthalmic clinic, piles of imaging techniques, such as optical coherence tomography (OCT), fundus autofluorescence (FAF), Scheimpflug imaging and ultrasound, have been widely and frequently used. Meanwhile, imaging technologies, as mentioned above, gradually enhanced our understanding and diagnosis accuracy of most ocular diseases and surgical complications.
Multi-modality ophthalmic imaging, defined as including two or more imaging modalities within the setting of a single examination, can significantly increase the accuracy in the detection, localization and characterization of many ocular diseases to optimize the management procedure. Moreover, as a beneficial imaging processing method, artificial intelligence, like other sophisticated image processing methods, has been widely used in medical imaging and extensive data analysis, especially for ocular disease diagnosis, monitoring, and treatment.
Some previous studies have highlighted the importance of optimizing treatment outcomes using different imaging methods. Therefore, as an extension and supplement of prior research, the current Research Topic will mainly focus on multi-modality imaging applications for improving refractive cataract surgical outcomes and understanding the potential mechanisms of retinal disorders.
This Research Topic welcomes up-to-date Original Research, Case Report, Review, and Systematic Review articles on multi-modal ophthalmic imaging in retinal disease and refractive cataract surgery, which includes, but is not limited to the following:
- Methods to optimize the diagnosis and treatment process of Cataracts and retinal disease
- Perspectives on surgical complications or anatomic structures shown by multi-modality ophthalmic imaging
- Innovation of ocular imaging technology and experimental verification
- Deep learning model development and validation
- Finite element model or simulation model for specific conditions
- Real clinical data to improve cataract surgical results and understanding of retinal disease
- New imaging biomarkers for retinal diseases
- Big data of multi-modal ophthalmic imaging
- Verification of surgical design improvement directed by multi-modality ophthalmic imaging
Cataracts and retinal disease (such as age-related macular degeneration, diabetic retinopathy, macular oedema etc.) are the leading causes of blindness worldwide, whose prevalence increases with age. More specifically, the prevalence of cataracts was 3.9% among 55–64 years to 92.6% among those 80 years and older; the prevalence of retinal disease was in the range of 51.26% to 53.05% for ages more than 60 years.
Refractive cataract surgery aims to remove cataracts while treating refractive errors using premium or advanced intraocular lenses to restore optimal vision and reduce the patient’s dependence on glasses. However, coexisting retinal disorders increase the incidence with risks of surgical complications and make the surgery more challenging. Therefore, optimizing preoperative, intraoperative, and postoperative management strategies could improve patients’ visual prognosis.
With the increasing dependence of clinicians on medical imaging in the ophthalmic clinic, piles of imaging techniques, such as optical coherence tomography (OCT), fundus autofluorescence (FAF), Scheimpflug imaging and ultrasound, have been widely and frequently used. Meanwhile, imaging technologies, as mentioned above, gradually enhanced our understanding and diagnosis accuracy of most ocular diseases and surgical complications.
Multi-modality ophthalmic imaging, defined as including two or more imaging modalities within the setting of a single examination, can significantly increase the accuracy in the detection, localization and characterization of many ocular diseases to optimize the management procedure. Moreover, as a beneficial imaging processing method, artificial intelligence, like other sophisticated image processing methods, has been widely used in medical imaging and extensive data analysis, especially for ocular disease diagnosis, monitoring, and treatment.
Some previous studies have highlighted the importance of optimizing treatment outcomes using different imaging methods. Therefore, as an extension and supplement of prior research, the current Research Topic will mainly focus on multi-modality imaging applications for improving refractive cataract surgical outcomes and understanding the potential mechanisms of retinal disorders.
This Research Topic welcomes up-to-date Original Research, Case Report, Review, and Systematic Review articles on multi-modal ophthalmic imaging in retinal disease and refractive cataract surgery, which includes, but is not limited to the following:
- Methods to optimize the diagnosis and treatment process of Cataracts and retinal disease
- Perspectives on surgical complications or anatomic structures shown by multi-modality ophthalmic imaging
- Innovation of ocular imaging technology and experimental verification
- Deep learning model development and validation
- Finite element model or simulation model for specific conditions
- Real clinical data to improve cataract surgical results and understanding of retinal disease
- New imaging biomarkers for retinal diseases
- Big data of multi-modal ophthalmic imaging
- Verification of surgical design improvement directed by multi-modality ophthalmic imaging
