An Ophthalmologist's Guide to Deciphering Studies in Artificial Intelligence
Authors: Daniel S W Ting, Aaron Y Lee, Tien Y Wong
Journal: Ophthalmology (2019)

In recent years, an influx of articles in medicine1LeCun Y. Bengio Y. Hinton G. Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (42113) Google Scholar and ophthalmology2Schmidt-Erfurth U. Sadeghipour A. Gerendas B.S. et al.Artificial intelligence in retina.Prog Retin Eye Res. 2018; 67: 1-29Crossref PubMed Scopus (341) Google Scholar, 3Ting D.S.W. Peng L. Varadarajan A.V. et al.Deep learning in ophthalmology: the technical and clinical considerations.Prog Retin Eye Res. 2019; 72 (Sep) (Epub 2019 Apr 29): 100759https://doi.org/10.1016/j.preteyeres.2019.04.003Crossref PubMed Scopus (198) Google Scholar, 4Lee A. Taylor P. Kalpathy-Cramer J. Tufail A. Machine learning has arrived! Ophthalmology. 2017; 124: 1726-1728Scopus (69) Google Scholar on artificial intelligence (AI) has appeared. Deep learning, a recently described AI machine learning technique, when applied to image analysis, allows the algorithm to analyze data using multiple processing layers to extract different image features,1LeCun Y. Bengio Y. Hinton G. Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (42113) Google Scholar with the lower processing layers recognizing basic features (e.g., the number and arrangement of edges of an image) and the higher layers identifying items more meaningful to human observers (e.g., nose, faces, disease lesions). In ophthalmology, many groups have reported exceptional diagnostic performance using deep learning algorithms to detect various ocular conditions based on anterior segment topography (e.g., keratoconus),5Hwang E.S. Perez-Straziota C.E. Kim S.W. et al.Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis.Ophthalmology. 2018; 125: 1862-1871Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar surgical videos (e.g., identification of phases in cataract surgeries),6Yu F. Croso G.S. Kim T.S. Assessment of automated identification of phases in videos of cataract surgery using machine learning and deep learning techniques.JAMA Netw Open. 2019; 2e191860Crossref PubMed Scopus (49) Google Scholar fundus photographs (e.g., diabetic retinopathy,7Gulshan V. Peng L. Coram M. et al.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA. 2016; 316: 2402-2410Crossref PubMed Scopus (3479) Google Scholar, 8Ting D.S.W. Cheung C.Y. Lim G. et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223Crossref PubMed Scopus (1002) Google Scholar, 9Gargeya R. Leng T. Automated identification of diabetic retinopathy using deep learning.Ophthalmology. 2017; 124: 962-969Abstract Full Text Full Text PDF PubMed Scopus (676) Google Scholar, 10Abramoff M.D. Lou Y. Erginay A. et al.Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning.Invest Ophthalmol Vis Sci. 2016; 57: 5200-5206Crossref PubMed Scopus (572) Google Scholar, 11Abramoff M.D. Lavin P.T. Birch M. et al.Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.NPJ Digital Medicine. 2018; 39: 1-8Google Scholar glaucoma,12Li Z. He Y. Keel S. et al.Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs.Ophthalmology. 2018; 125: 1199-1206Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar age-related macular degeneration,13Burlina P.M. Joshi N. Pekala M. et al.Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks.JAMA Ophthalmol. 2017; 135: 1170-1176Crossref PubMed Scopus (333) Google Scholar, 14Burlina P. Joshi N. Pacheco K.D. et al.Utility of deep learning methods for referability classification of age-related macular degeneration.JAMA Ophthalmol. 2018; 136: 1305-1307Crossref PubMed Scopus (32) Google Scholar, 15Peng Y. Dharssi S. Chen Q. et al.DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs.Ophthalmology. 2019; 126: 565-575Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar, 16De Fauw J. Ledsam J.R. Romera-Paredes B. et al.Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat Med. 2018; 24: 1342-1350Crossref PubMed Scopus (1126) Google Scholar and retinopathy of prematurity17Redd T.K. Campbell J.P. Brown J.M. et al.Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.Br J Ophthalmol. 2018; (Nov 23. pii: bjophthalmol-2018-313156) ([Epub ahead of print])https://doi.org/10.1136/bjophthalmol-2018-313156Crossref PubMed Scopus (67) Google Scholar, 18Brown J.M. Campbell J.P. Beers A. et al.Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks.JAMA Ophthalmol. 2018; 136: 803-810Crossref PubMed Scopus (283) Google Scholar), and anterior and posterior segment OCT (e.g., glaucoma19Fu H. Baskaran M. Xu Y. et al.A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images.Am J Ophthalmol. 2019; 203: 37-45Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar and multiple retinal diseases16De Fauw J. Ledsam J.R. Romera-Paredes B. et al.Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat Med. 2018; 24: 1342-1350Crossref PubMed Scopus (1126) Google Scholar). However, a common concern is that these articles have varying standards and often lack an agreed on, standardized format with respect to presenting methods, statistics, reporting metrics, and clinical translational value. Furthermore, many readers may not understand the nuances and technical details of this relatively new area of research in ophthalmology. How should readers read the AI research articles more effectively and apply them in the context of prior and current work? As editors of different journals, we have been able to review a number of high-quality articles. Herein, we share some pointers and insights to help readers to understand better, to appraise critically, and to evaluate the data presented in AI articles in ophthalmology. First, in the introduction of an article, high-quality studies usually state clearly the unmet clinical or public health need and the primary research question that needs to be tackled by AI technology. A clear example is the role of AI to screen for diabetic retinopathy (DR). At present, DR screening is performed commonly by humans (e.g., ophthalmologists, optometrists, orthoptists, or trained nonmedical personnel), and often in countries with well-established and existing DR screening programs such as the United States, United Kingdom, and Singapore. With a projected rise in diabetes prevalence,20Yau J.W. Rogers S.L. Kawasaki R. et al.Global prevalence and major risk factors of diabetic retinopathy.Diabetes Care. 2012; 35: 556-564Crossref PubMed Scopus (2746) Google Scholar many countries do not have established DR screening programs (e.g., China, Indonesia, and many countries in South America, Africa, and Asia). To help address the limited and difficult-to-sustain resource in screening capacity, manpower costs, DR grading expertise, or a combination thereof, AI may be a helpful solution for some countries, even in those with existing screening programs. Given the different regulations for health information technology in each country, the variety of deployment models, and varying thresholds for referable DR, careful consideration is required before adoption. As part of an article's introduction, a comprehensive literature search often is available regarding information on similar technologies relevant to the specific diseases and the so-called value add of the proposed AI system. Based on this information, the readers could better understand the clinical gap that the proposed AI algorithm may bridge—for example, its application on specific target populations and clinical settings—using different classification outputs on fundus photographs (e.g., different ocular diseases, such as DR, glaucoma, or AMD8Ting D.S.W. Cheung C.Y. Lim G. et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223Crossref PubMed Scopus (1002) Google Scholar), different severity levels of a specific disease (e.g., early vs. late glaucoma21Ahn J.M. Kim S. Ahn K.S. et al.A deep learning model for the detection of both advanced and early glaucoma using fundus photography.PLoS One. 2018; 13e0207982Crossref PubMed Scopus (5) Google Scholar), or the same imaging device with different diagnostic outcomes (e.g., detection of specific retinal lesions22Lee C.S. Baughman D.M. Lee A.Y. Deep learning is effective for classifying normal versus age-related macular degeneration OCT images.Ophthalmol Retina. 2017; 1: 322-327Abstract Full Text Full Text PDF PubMed Scopus (318) Google Scholar, 23Schmidt-Erfurth U. Bogunovic H. Sadeghipour A. et al.Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration.Ophthalmol Retina. 2018; 2: 24-30Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar vs. triaging referral decisions using OCT16De Fauw J. Ledsam J.R. Romera-Paredes B. et al.Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat Med. 2018; 24: 1342-1350Crossref PubMed Scopus (1126) Google Scholar). Many of the ophthalmology and clinician readers, without prior computer science background, may feel overwhelmed with the methods section of AI studies because of the use of sophisticated technical terminology. Although it may not be necessary for readers to understand fully the entire technical architecture or mathematical formulas, it is important to appreciate the key components of the AI systems that are essential for clinical translation. Broadly speaking, an AI system consists of 2 phases: (1) training and validation and (2) testing. For training and validation, the AI system requires 2 main components: (1) a training dataset consisting of clinical data and images and (2) selection of a technical network (also known as convolutional neural network [CNN]). More commonly, most of the dataset will be used for training and validation, followed by testing (e.g., 60%/20%/20%; 70%/20%/10%, or 80%/10%/10% respectively), ideally at patients' level with no overlaps of the same data and image in any phases to avoid images from the same patient being used in the training and testing phase. Regardless of the split in the training and validation datasets, it is more important to have sufficiently powered, and preferably several independent, testing datasets (which are discussed below). A similar analogy might be that a basketball player could have different ways or styles of training, but he or she would make a great basketball player if he or she consistently performs well against players he or she has never seen before. For training and testing, it is important to have a sufficiently large and robust clinical dataset. Many readers may ask, "What would be the sufficient number or sample size required to train an AI algorithm?" But these numbers vary depending on how obvious or unique a condition is. In principle, conditions that have obvious, unique, and easily distinguishable characteristics (e.g., neovascular AMD and proliferative DR on fundus photographs) may require less training examples compared with conditions that have abstract or subtle changes (early AMD with drusen or mild nonproliferative DR). Similarly, smaller OCT datasets are needed to train for large hemorrhagic pigment epithelial detachment or full-thickness macular hole compared with a subtle sliver of subretinal fluid in AMD or mild diabetic macular edema. In contrast, conditions that may have similar features require a larger dataset for training (e.g., differentiation between hard exudates and drusen, microaneurysms vs. small dot hemorrhages or pigments, severe nonproliferative DR vs. central retinal vein occlusions on fundus photographs; cystoid macular edema secondary to different causes related to DR, AMD, and retinal vein occlusions on OCT). Thus, there is no simple answer to the question of minimum sample size because, apart from the above-mentioned factors, it also depends on the specific research questions, technical methods, and desired clinical output. Based on our observations, high-quality AI articles published in the major journals use large data and image banks for AI algorithm development,7Gulshan V. Peng L. Coram M. et al.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA. 2016; 316: 2402-2410Crossref PubMed Scopus (3479) Google Scholar, 8Ting D.S.W. Cheung C.Y. Lim G. et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223Crossref PubMed Scopus (1002) Google Scholar, 16De Fauw J. Ledsam J.R. Romera-Paredes B. et al.Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat Med. 2018; 24: 1342-1350Crossref PubMed Scopus (1126) Google Scholar although some technical approaches (e.g., transfer learning) have been shown to reduce the size of training datasets (this is discussed further below).24Ting D.S.W. Liu Y. Burlina P. et al.AI for medical imaging goes deep.Nat Med. 2018; 24: 539-540Crossref PubMed Scopus (93) Google Scholar Apart from the actual number of data points or images, it is also important to understand exactly the number of eyes being compared with the number of patients involved in the training. This is because some AI algorithms may be trained on thousands of data points or images, but only from several hundred patients because these images or data were collected repetitively from the same patients. Such seemingly large datasets may lack diversity and potential generalizability to other populations. As in other studies, the study design and methods of recruitment (retrospective or prospective, inclusion and exclusion criteria, randomized trial vs. cohort study) also are essential components for understanding the robustness of the training datasets. In particular, the reference standard (or also known as ground truth in technical terminology) acts as the so-called brain for the AI algorithms. Thus, it is important to find out how the reference standard was derived. Many reference standards are based on human diagnosis and assessment. So, for image-based research, the professional background and training, the experience, and the number of the human assessors should be specified. A high-quality reference standard could consist of board-certified ophthalmologists and fellowship-trained subspecialists, certified nonmedical professional graders, or optometrists in reading centers who have undertaken intensive training and accreditation with reproducible and consistent outcomes. This essential step avoids the so-called garbage-in, garbage-out circumstance. There is some debate as to whether ophthalmologists or subspecialists should be considered as the reference standard because increasingly, reading centers have used professional graders as the gold standard for many clinical trials.25Wells J.A. Glassman A.R. et al.Diabetic Retinopathy Clinical Research NetworkAflibercept, bevacizumab, or ranibizumab for diabetic macular edema.N Engl J Med. 2015; 372: 1193-1203Crossref PubMed Scopus (1077) Google Scholar, 26Sivaprasad S. Prevost A.T. Vasconcelos J.C. et al.Clinical efficacy of intravitreal aflibercept versus panretinal photocoagulation for best corrected visual acuity in patients with proliferative diabetic retinopathy at 52 weeks (CLARITY): a multicentre, single-blinded, randomised, controlled, phase 2b, non-inferiority trial.Lancet. 2017; 389: 2193-2203Abstract Full Text Full Text PDF PubMed Scopus (203) Google Scholar, 27Gross J.G. Glassman A.R. et al.Writing Committee for the Diabetic Retinopathy Clinical Research NetworkPanretinal photocoagulation vs intravitreous ranibizumab for proliferative diabetic retinopathy: a randomized clinical trial.JAMA. 2015; 314: 2137-2146Crossref PubMed Scopus (509) Google Scholar In AI studies, many potential technical methods are available to train an algorithm, and this again depends largely on the research question and the type of training dataset. Generally, the training dataset can be based on clinical data only, image only, or multimodal models (clinical data plus image plus genetic and serum biomarkers). To analyze clinical data (e.g., electronic health records or population-based studies), many research groups have used conventional machine learning methods (e.g., random forest or support vector machines) and statistical methods (e.g., multivariate logistic regression or generalized linear mixed models) to train the model because these conventional methods seem sufficient to generate robust predictive algorithms. It is possible to adopt the newer deep learning methods for longitudinal and predictive clinical problems that have a temporal sequence.28Rajkomar A. Oren E. Chen K. et al.Scalable and accurate deep learning with electronic health records.NPJ Digit Med. 2018; 1: 18Crossref PubMed Scopus (987) Google Scholar, 29Wen J.C. Lee C.S. Keane P.A. et al.Forecasting future Humphrey visual fields using deep learning.PLoS One. 2019; 14e0214875Crossref PubMed Scopus (67) Google Scholar In contrast, for image-based training data (fundus photographs or OCT), deep learning is the most popular technique thus far because of increased diagnostic performance. Readers may come across CNNs, which are deep learning algorithms consisting of neuron-like computational layers that can have varying numbers of layers. The popular CNNs consists of AlexNet, VGGNet, Inception V4, ResNet, and DenseNet,3Ting D.S.W. Peng L. Varadarajan A.V. et al.Deep learning in ophthalmology: the technical and clinical considerations.Prog Retin Eye Res. 2019; 72 (Sep) (Epub 2019 Apr 29): 100759https://doi.org/10.1016/j.preteyeres.2019.04.003Crossref PubMed Scopus (198) Google Scholar and these CNNs are commonly found off the shelf and can be downloaded from public domains. To enhance the diagnostic performance further, a technique known as transfer learning has been used. In this method, CNNs usually are pretrained with the ImageNet database (consisting of millions of images such as cars, animals, etc.) before being applied to the specific dataset in question. The value-add of this approach is reduced if the training datasets comprise sufficient numbers. It is usually not necessary for readers to understand fully how these CNNs are constructed mathematically, but it is important to understand the next crucial step in the methodology: the overall operational flow of the CNN within an AI system. An easy to understand and user-friendly AI system ideally would describe the operational workflow to the readers. This is similar to describing a patient journey from registration, to clinical consultation, to diagnosis. First, in terms of AI registration, the readers first can discover the ability of an AI system to detect the types of ocular images (anterior segment, fundus, and OCT photographs), field of view (macula-centered vs. optic disc centered), and gradability (gradable vs. nongradable). Second, as soon as the AI input has been registered, it is seen by the "doctors" (in this case, the AI algorithm) for diagnosis and consultation. During this step, the image can be preprocessed (cropping, contrast enhancement, etc.), followed by the analysis of the AI system (CNNs) and generation of diagnosis (output classifications, sometimes binary but sometimes multiclass outputs). Finally, some groups also have reported using visualization maps to highlight the abnormal areas detected by the AI system,30Sayres R. Taly A. Rahimy E. et al.Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy.Ophthalmology. 2019; 126: 552-564Abstract Full Text Full Text PDF PubMed Scopus (168) Google Scholar serving as good clinical decision support tools for clinical implementation. However, similar steps may not be applicable to other nonimage methods (e.g., visual field, clinical data). Earlier this year, the United States Food and Drug Administration published a framework entitled, "Proposed Regulatory Framework for Modifications for Artificial Intelligence/Machine Learning-Based Software as a Medical Device (SaMD)," using the definition by the International Medical Device Regulators Forum to consider AI-based software as a medical device.31United States Food and Drug AdministrationProposed regulatory framework for modifications for artificial intelligence/machine learning-based software as a medical device (SaMD).https://www.fda.gov/media/122535/downloadDate: 2019Date accessed: August 17, 2019Google Scholar Apart from advocating good-quality systems and machine learning practices, it also states the importance of evaluating the AI system for validity in having correct AI input data to generate accurate output data and between the AI output and target condition or intended purposes in clinical care. For clinical adoption, it is useful to look at the disease prevalence on the described testing datasets and to better understand whether it is conducted in a population-based or clinic-based setting. The sample size power calculation for testing datasets may include the disease prevalence and type 1 and 2 errors with the 95% confidence intervals, similar to other clinical trials or diagnostic tests. Showing the reproducibility of an AI system in an out-of-sample, geographically distinct population also would increase the quality of the presented work. Apart from that, it is also important for the readers to pay attention to the disease classification systems adopted in the AI studies (e.g., functional vs. structural diagnosis for glaucoma,32Quigley H.A. Glaucoma.Lancet. 2011; 377: 1367-1377Abstract Full Text Full Text PDF PubMed Scopus (878) Google Scholar the United Kingdom National Health Service Diabetic Retinopathy classifications33Harding S. Greenwood R. Aldington S. et al.Grading and disease management in national screening for diabetic retinopathy in England and Wales.Diabet Med. 2003; 20: 965-971Crossref PubMed Scopus (147) Google Scholar vs. the International Classification Diabetic Retinopathy severity scales).34Wilkinson C.P. Ferris 3rd, F.L. Klein R.E. et al.Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales.Ophthalmology. 2003; 110: 1677-1682Abstract Full Text Full Text PDF PubMed Scopus (2280) Google Scholar Many AI studies report the area under the receiver operating characteristic curve, a popular statistical method that most clinicians are familiar with, although a precision-recall curve (a method that is used commonly in the technical or machine learning world) may be more appropriate for imbalanced datasets.35Davis J.L. G M. The relationship between precision-recall and ROC curves.Proceedings of the 23rd International Conference on Machine Learning. 2006; : 233-240Google Scholar From what we have observed, many high-quality AI articles describe the rationale for how an operating threshold was determined in the training dataset and demonstrate the sensitivity and specificity on the independent datasets performed on same operating threshold. We also have observed that the accuracy, positive predictive value, negative predictive value, and Cohen's κ value may be reported, although this may not be as popular as the traditional metrics of area under the receiver operating characteristic curve, sensitivity, and specificity. The recommended reporting metrics sometimes are included as tables and figures, especially if there are multiple independent testing datasets.8Ting D.S.W. Cheung C.Y. Lim G. et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223Crossref PubMed Scopus (1002) Google Scholar Given the variability of the reporting metrics, a consensus on the best metrics for ophthalmology-related AI research may be warranted. To help readers understand how the AI system can be integrated and adopted within clinical practice, high-quality AI articles generally include limitations of the specific AI systems (e.g., the AI system can detect only high-quality images or lacks the ability to diagnose other conditions). Many AI research groups conduct due diligence on their system, especially when they were tested only in research settings. In fact, we encourage transparency in reporting to expedite clinical translation of AI technology. As the field evolves, we must determine and reach a consensus in deciding how best to evaluate and critically appraise AI research in ophthalmology to ensure robustness and reproducibility of algorithms and generalizability of study findings to real-world clinical settings. For diagnostic tests, the Standards for Reporting of Diagnostic Accuracy steering committee has proposed a list of 25 items to standardize the reporting in diagnostic tests studies.36Bossuyt P.M. Reitsma J.B. Bruns D.E. et al.Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD Initiative.Ann Intern Med. 2003; 138: 40-44Crossref PubMed Scopus (667) Google Scholar Artificial intelligence algorithms also could be considered as novel diagnostic tests. Hence, a good-quality study could include most of the Standards for Reporting of Diagnostic Accuracy criteria, with additional information on the training methodologies. Furthermore, a number of generic AI guidelines (not specific to ophthalmology) currently are under development, such as the AI extension of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis—Artificial Intelligence).37Collins G.S. Reitsma J.B. Altman D.G. Moons K.G. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.Ann Intern Med. 2015; 162: 55-63Crossref PubMed Scopus (1224) Google Scholar Furthermore, it is worth noting that in traditional biomarker research, the term "validation" is generally used to confirm the diagnostic value of a biomarker, which is dissimilar to what is used in the AI settings (cross-validation is used as part of the training phase as mentioned earlier). Nevertheless, many AI groups have used different terminologies as the "testing" phase and this may potentially create some confusion to the readers when they browse through the articles. Our editorial summarizes some observations that may help readers navigate, review, and appreciate AI articles in ophthalmology (see Table S1, available at www.aaojournal.org). The rapid emergence of this area of research and the lack of standardized reporting format and standards suggest the need for a formation of a working task force to develop consensus and guidelines for AI research in ophthalmology, possibly carried out by relevant experts using a Delphi approach. This framework may help guide researchers, clinicians, and policy makers to evaluate, apply, and better translate AI technologies in ophthalmology and to harness the power of the big data revolution that will transform healthcare. Download .docx (.03 MB) Help with docx files Table S1