diff --git a/manuscript/rsf.bib b/manuscript/rsf.bib
index 5043d120da2bef08ee4a4bedc7e3272233a0ebe0..d6c33bc30cf19e2d4f7c5c3bdf75c8533e7b0aff 100644
--- a/manuscript/rsf.bib
+++ b/manuscript/rsf.bib
@@ -261,3 +261,295 @@
   year={1991},
   doi={10.1126/science.1957169}
 }
+
+@article{fujimoto2016development,
+  title={The development, commercialization, and impact of optical coherence tomography},
+  author={Fujimoto, James and Swanson, Eric},
+  journal={Investigative ophthalmology \& visual science},
+  volume={57},
+  number={9},
+  pages={OCT1--OCT13},
+  year={2016},
+  publisher={The Association for Research in Vision and Ophthalmology}
+}
+
+@article{fujimoto2000optical,
+  title={Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy},
+  author={Fujimoto, James G and Pitris, Costas and Boppart, Stephen A and Brezinski, Mark E},
+  journal={Neoplasia},
+  volume={2},
+  number={1-2},
+  pages={9--25},
+  year={2000},
+  publisher={Elsevier}
+}
+
+@article{alexopoulos2022development,
+  title={The development and clinical application of innovative optical ophthalmic imaging techniques},
+  author={Alexopoulos, Palaiologos and Madu, Chisom and Wollstein, Gadi and Schuman, Joel S},
+  journal={Frontiers in medicine},
+  volume={9},
+  pages={891369},
+  year={2022},
+  publisher={Frontiers Media SA}
+}
+
+@article{ali2021optical,
+  title={Optical coherence tomography’s current clinical medical and dental applications: a review},
+  author={Ali, Saqib and Gilani, Saqlain Bin Syed and Shabbir, Juzer and Almulhim, Khalid S and Bugshan, Amr and Farooq, Imran},
+  journal={F1000Research},
+  volume={10},
+  year={2021},
+  publisher={Faculty of 1000 Ltd}
+}
+
+@article{reynolds2021coronary,
+  title={Coronary optical coherence tomography and cardiac magnetic resonance imaging to determine underlying causes of myocardial infarction with nonobstructive coronary arteries in women},
+  author={Reynolds, Harmony R and Maehara, Akiko and Kwong, Raymond Y and Sedlak, Tara and Saw, Jacqueline and Smilowitz, Nathaniel R and Mahmud, Ehtisham and Wei, Janet and Marzo, Kevin and Matsumura, Mitsuaki and others},
+  journal={Circulation},
+  volume={143},
+  number={7},
+  pages={624--640},
+  year={2021},
+  publisher={Am Heart Assoc}
+}
+
+@article{starovoyt2019high,
+  title={High-resolution imaging of the human cochlea through the round window by means of optical coherence tomography},
+  author={Starovoyt, Anastasiya and Putzeys, Tristan and Wouters, Jan and Verhaert, Nicolas},
+  journal={Scientific reports},
+  volume={9},
+  number={1},
+  pages={14271},
+  year={2019},
+  publisher={Nature Publishing Group UK London}
+}
+
+@article{chen2021evaluation,
+  title={Evaluation of ultrahigh-resolution optical coherence tomography for basal cell carcinoma, seborrheic keratosis, and nevus},
+  author={Chen, Shufen and Xie, Fang and Hao, Tian and Xie, Jun and Wang, Xianghong and Chen, Si and Liu, Linbo and Li, Chengxin},
+  journal={Skin Research and Technology},
+  volume={27},
+  number={4},
+  pages={479--485},
+  year={2021},
+  publisher={Wiley Online Library}
+}
+
+@article{hariri2013estimation,
+  title={Estimation of the enamel and dentin mineral content from the refractive index},
+  author={Hariri, Ilnaz and Sadr, Alireza and Nakashima, Syozi and Shimada, Yasushi and Tagami, Junji and Sumi, Yasunori},
+  journal={Caries research},
+  volume={47},
+  number={1},
+  pages={18--26},
+  year={2013},
+  publisher={S. Karger AG}
+}
+
+@article{chan2023eyes,
+  title={Eyes as the windows into cardiovascular disease in the era of big data},
+  author={Chan, Yarn Kit and Cheng, Ching-Yu and Sabanayagam, Charumathi},
+  journal={Taiwan Journal of Ophthalmology},
+  volume={13},
+  number={2},
+  pages={151--167},
+  year={2023},
+  publisher={Medknow}
+}
+
+@article{suh2023retina,
+  title={Retina oculomics in neurodegenerative disease},
+  author={Suh, Alex and Ong, Joshua and Kamran, Sharif Amit and Waisberg, Ethan and Paladugu, Phani and Zaman, Nasif and Sarker, Prithul and Tavakkoli, Alireza and Lee, Andrew G},
+  journal={Annals of Biomedical Engineering},
+  volume={51},
+  number={12},
+  pages={2708--2721},
+  year={2023},
+  publisher={Springer}
+}
+
+@article{lin2024individual,
+  title={Individual prognostication of disease activity and disability worsening in multiple sclerosis with retinal layer thickness z scores},
+  author={Lin, Ting-Yi and Motamedi, Seyedamirhosein and Asseyer, Susanna and Chien, Claudia and Saidha, Shiv and Calabresi, Peter A and Fitzgerald, Kathryn C and Samadzadeh, Sara and Villoslada, Pablo and Llufriu, Sara and others},
+  journal={Neurology: Neuroimmunology \& Neuroinflammation},
+  volume={11},
+  number={5},
+  pages={e200269},
+  year={2024},
+  publisher={Lippincott Williams \& Wilkins Hagerstown, MD}
+}
+
+@article{nusinovici2022retinal,
+  title={Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk},
+  author={Nusinovici, Simon and Rim, Tyler Hyungtaek and Yu, Marco and Lee, Geunyoung and Tham, Yih-Chung and Cheung, Ning and Chong, Crystal Chun Yuen and Da Soh, Zhi and Thakur, Sahil and Lee, Chan Joo and others},
+  journal={Age and ageing},
+  volume={51},
+  number={4},
+  pages={afac065},
+  year={2022},
+  publisher={Oxford University Press}
+}
+
+@article{tham2014global,
+  title={Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis},
+  author={Tham, Yih-Chung and Li, Xiang and Wong, Tien Y and Quigley, Harry A and Aung, Tin and Cheng, Ching-Yu},
+  journal={Ophthalmology},
+  volume={121},
+  number={11},
+  pages={2081--2090},
+  year={2014},
+  publisher={Elsevier}
+}
+
+@article{chen2024deep,
+  title={Deep-Learning-Based Group Pointwise Spatial Mapping of Structure to Function in Glaucoma},
+  author={Chen, Zhiqi and Ishikawa, Hiroshi and Wang, Yao and Wollstein, Gadi and Schuman, Joel S},
+  journal={Ophthalmology Science},
+  volume={4},
+  number={5},
+  pages={100523},
+  year={2024},
+  publisher={Elsevier}
+}
+
+@article{thirunavukarasu2024validated,
+  title={A validated web-application (GFDC) for automatic classification of glaucomatous visual field defects using Hodapp-Parrish-Anderson criteria},
+  author={Thirunavukarasu, Arun James and Jain, Nikhil and Sanghera, Rohan and Lattuada, Federico and Mahmood, Shathar and Economou, Anna and Yu, Helmut CY and Bourne, Rupert},
+  journal={npj Digital Medicine},
+  volume={7},
+  number={1},
+  pages={131},
+  year={2024},
+  publisher={Nature Publishing Group UK London}
+}
+
+@article{wollstein2012retinal,
+  title={Retinal nerve fibre layer and visual function loss in glaucoma: the tipping point},
+  author={Wollstein, Gadi and Kagemann, Larry and Bilonick, Richard A and Ishikawa, Hiroshi and Folio, Lindsey S and Gabriele, Michelle L and Ungar, Allison K and Duker, Jay S and Fujimoto, James G and Schuman, Joel S},
+  journal={British Journal of Ophthalmology},
+  volume={96},
+  number={1},
+  pages={47--52},
+  year={2012},
+  publisher={BMJ Publishing Group Ltd}
+}
+
+@article{SCHUMAN2022e3,
+title = {A Case for the Use of Artificial Intelligence in Glaucoma Assessment},
+journal = {Ophthalmology Glaucoma},
+volume = {5},
+number = {3},
+pages = {e3-e13},
+year = {2022},
+issn = {2589-4196},
+doi = {https://doi.org/10.1016/j.ogla.2021.12.003},
+url = {https://www.sciencedirect.com/science/article/pii/S2589419621002805},
+author = {Joel S. Schuman and Maria {De Los Angeles Ramos Cadena} and Rebecca McGee and Lama A. Al-Aswad and Felipe A. Medeiros and Michael Abramoff and Mark Blumenkranz and Emily Chew and Michael Chiang and Malvina Eydelman and David Myung and Carol Shields and Bhavna J. Antony and Tin Aung and Michael Boland and Tom Brunner and Robert T. Chang and Balwantray Chauhan and D. Hunter Cherwek and David Garway-Heath and Adrienne Graves and Jeffrey L. Goldberg and Minguang He and Naama Hammel and Donald Hood and Hiroshi Ishikawa and Chris Leung and Louis Pasquale and Harry A. Quigley and Calvin W. Roberts and Alan L. Robin and Elena Sturman and Remo Susanna and Jayme Vianna and Linda Zangwill},
+keywords = {Artificial intelligence, Deep learning, Glaucoma, Neural networks, OCT},
+abstract = {We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, “The Future of Artificial Intelligence–Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways,” held virtually September 3–4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018–2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.}
+}
+
+@article{prahs2018oct,
+  title={OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications},
+  author={Prahs, Philipp and Radeck, Viola and Mayer, Christian and Cvetkov, Yordan and Cvetkova, Nadezhda and Helbig, Horst and M{\"a}rker, David},
+  journal={Graefe's Archive for Clinical and Experimental Ophthalmology},
+  volume={256},
+  pages={91--98},
+  year={2018},
+  publisher={Springer}
+}
+
+@article{grewal2008artificial,
+  title={Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis},
+  author={Grewal, DS and Jain, R and Grewal, SPS and Rihani, V},
+  journal={European journal of ophthalmology},
+  volume={18},
+  number={6},
+  pages={915--921},
+  year={2008},
+  publisher={SAGE Publications Sage UK: London, England}
+}
+
+@article{shi2024rnflt2vec,
+  title={RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps},
+  author={Shi, Min and Tian, Yu and Luo, Yan and Elze, Tobias and Wang, Mengyu},
+  journal={Medical Image Analysis},
+  volume={94},
+  pages={103110},
+  year={2024},
+  publisher={Elsevier}
+}
+
+@article{berenguer2021automatic,
+  title={Automatic segmentation of the retinal nerve fiber layer by means of mathematical morphology and deformable models in 2d optical coherence tomography imaging},
+  author={Berenguer-Vidal, Rafael and Verd{\'u}-Monedero, Rafael and Morales-S{\'a}nchez, Juan and Sell{\'e}s-Navarro, Inmaculada and Del Amor, Roc{\'\i}o and Garc{\'\i}a, Gabriel and Naranjo, Valery},
+  journal={Sensors},
+  volume={21},
+  number={23},
+  pages={8027},
+  year={2021},
+  publisher={MDPI}
+}
+
+@article{christopher2018retinal,
+  title={Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression},
+  author={Christopher, Mark and Belghith, Akram and Weinreb, Robert N and Bowd, Christopher and Goldbaum, Michael H and Saunders, Luke J and Medeiros, Felipe A and Zangwill, Linda M},
+  journal={Investigative ophthalmology \& visual science},
+  volume={59},
+  number={7},
+  pages={2748--2756},
+  year={2018},
+  publisher={The Association for Research in Vision and Ophthalmology}
+}
+
+@article{tan2024hybrid,
+  title={A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions},
+  author={Tan, Ou and Greenfield, David S and Francis, Brian A and Varma, Rohit and Schuman, Joel S and Huang, David and Choi, Dongseok},
+  journal={ArXiv},
+  year={2024},
+  publisher={arXiv}
+}
+
+@article{kingma2014adam,
+  title={Adam: A method for stochastic optimization},
+  author={Kingma, Diederik P and Ba, Jimmy},
+  journal={arXiv preprint arXiv:1412.6980},
+  year={2014}
+}
+
+@article{chen2023segmentation,
+  title={Segmentation-free OCT-volume-based deep learning model improves pointwise visual field sensitivity estimation},
+  author={Chen, Zhiqi and Shemuelian, Eitan and Wollstein, Gadi and Wang, Yao and Ishikawa, Hiroshi and Schuman, Joel S},
+  journal={Translational Vision Science \& Technology},
+  volume={12},
+  number={6},
+  pages={28--28},
+  year={2023},
+  publisher={The Association for Research in Vision and Ophthalmology}
+}
+
+@article{george2020attention,
+  title={Attention-guided 3D-CNN framework for glaucoma detection and structural-functional association using volumetric images},
+  author={George, Yasmeen and Antony, Bhavna J and Ishikawa, Hiroshi and Wollstein, Gadi and Schuman, Joel S and Garnavi, Rahil},
+  journal={IEEE journal of biomedical and health informatics},
+  volume={24},
+  number={12},
+  pages={3421--3430},
+  year={2020},
+  publisher={IEEE}
+}
+
+@article{saini2022assessing,
+  title={Assessing surface shapes of the optic nerve head and peripapillary retinal nerve fiber layer in glaucoma with artificial intelligence},
+  author={Saini, Chhavi and Shen, Lucy Q and Pasquale, Louis R and Boland, Michael V and Friedman, David S and Zebardast, Nazlee and Fazli, Mojtaba and Li, Yangjiani and Eslami, Mohammad and Elze, Tobias and others},
+  journal={Ophthalmology Science},
+  volume={2},
+  number={3},
+  pages={100161},
+  year={2022},
+  publisher={Elsevier}
+}
+
+
+