Gilbert Lim | Duke-NUS Graduate Medical School (original) (raw)

Papers by Gilbert Lim

IntroductionAutomated machine learning (autoML) removes technical and technological barriers to b... more IntroductionAutomated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.MethodsThis review adhered to a PROSPERO-registered protocol (CRD42022344427). The Cochrane Library, Embase, MEDLINE, and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as-required arbitration by a third researcher.ResultsIn 82 studies, 26 distinct autoML platforms featured. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: AUCROC 0.35-1.00, F1-score 0.16-0.99,...

Eye and vision, Mar 11, 2022

Frontiers in Medicine, Oct 13, 2022

Background: Many artificial intelligence (AI) studies have focused on development of AI models, n... more Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Gunasekeran et al. APPRAISE Study: AI in Ophthalmology Methods: This was a multinational survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Current Opinion in Ophthalmology, Jul 26, 2021

Purpose of review Myopia is one of the leading causes of visual impairment, with a projected incr... more Purpose of review Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. Recent findings There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. Summary Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

Current Opinion in Ophthalmology

Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL... more Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. Recent findings Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. Summary We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

Automated machine learning (AutoML) allows for the simplified application of machine learning to ... more Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

Frontiers in Medicine

IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairm... more IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale.MethodsTo build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotyp...

npj Digital Medicine

Our study aims to identify children at risk of developing high myopia for timely assessment and i... more Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6–12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms – image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ −6.00 diopter) during teenage years (5 years later, age 11–17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93–0.95; Test dataset 0.91–0.93), clinical models (Prim...

Nanoscience & Nanotechnology Series

Eye and Vision, 2022

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ... more The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. ...

Journal of Clinical Ultrasound, 2022

The initial screening of medical images remains a fundamental bottleneck to the effective diagnos... more The initial screening of medical images remains a fundamental bottleneck to the effective diagnosis of time-sensitive diseases in many settings. This has been mitigated in recent years by the introduction of deep learning-based approaches, in many imaging fields. Perhaps one of the most popular applications has been toward replicating the judgment of human experts as closely as possible, with reference to some established scoring standard. This is the approach that Dong et al. have taken, in their study on classifying metacarpophalangeal (MCP) synovial proliferation (SP) in rheumatoid arthritis, from ultrasound images. The relevant scoring system is then the OMERACT-EULAR Synovitis Scoring (OESS) standard, which grades SP in a range of L0– L3. Area under receiver operating characteristic curve of around 0.90 were reported for the clinically relevant binary classification tasks of distinguishing between L0 and non-L0, and L0/L1 versus L2/L3.

Computer Vision – ACCV 2018 Workshops, 2019

As the application of deep learning (DL) advances in the healthcare sector, the need for simultan... more As the application of deep learning (DL) advances in the healthcare sector, the need for simultaneous, multi-annotated database of medical images for evaluations of novel DL systems grows. This study looked at DL algorithms that distinguish retinal images by the side of the eyes (Left and Right side) as well as the field positioning (Macular-centred or Optic Disc-centred) and evaluated these algorithms against a large dataset comprised of 7,953 images from multi-ethnic populations. For these convolutional neural networks, L/R model and Mac/OD model, a high AUC (0.978, 0.990), sensitivity (95.9%, 97.6%), specificity (95.5%, 96.7%) and accuracy (95.7%, 97.2%) were found, respectively, for the primary validation sets. The models presented high performance also using the external validation database.

2018 25th IEEE International Conference on Image Processing (ICIP), 2018

Vessel type classification is a preliminary step in quantifying the severity of various diseases.... more Vessel type classification is a preliminary step in quantifying the severity of various diseases. This paper proposes DBA, a simple yet effective vessel type classification method based on the principle that arteries are brighter than veins at the local scale. The weighted local difference of the red channel intensity of the main trunk of each vessel is compared with that of its two immediately neighbouring vessels, a feature that is highly correlated with vessel-rectified oxygen capacity, and in turn, vessel type. Experiments on the publicly-available INSPIRE-AVR and DRIVE datasets obtained average vessel accuracies of 0.9217/0.9071, and average pixel accuracies of 0.9602/0.9634 respectively, with particular effectiveness on images with low contrast, non-uniform illumination and colour variation confirmed on the SiMES 1 dataset.

Computer Vision – ACCV 2018 Workshops, 2019

End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal... more End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal image analysis tasks. However, such models’ generalizability to data from new sources may be less than optimal. We highlight some benefits of introducing intermediate goals in deep learning-based models.

Investigative Ophthalmology & Visual Science, 2018

Investigative Ophthalmology & Visual Science, 2019

IntroductionAutomated machine learning (autoML) removes technical and technological barriers to b... more IntroductionAutomated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.MethodsThis review adhered to a PROSPERO-registered protocol (CRD42022344427). The Cochrane Library, Embase, MEDLINE, and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and as-required arbitration by a third researcher.ResultsIn 82 studies, 26 distinct autoML platforms featured. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: AUCROC 0.35-1.00, F1-score 0.16-0.99,...

Eye and vision, Mar 11, 2022

Frontiers in Medicine, Oct 13, 2022

Background: Many artificial intelligence (AI) studies have focused on development of AI models, n... more Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Gunasekeran et al. APPRAISE Study: AI in Ophthalmology Methods: This was a multinational survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Current Opinion in Ophthalmology, Jul 26, 2021

Purpose of review Myopia is one of the leading causes of visual impairment, with a projected incr... more Purpose of review Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. Recent findings There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. Summary Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

Current Opinion in Ophthalmology

Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL... more Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. Recent findings Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. Summary We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

Automated machine learning (AutoML) allows for the simplified application of machine learning to ... more Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

Frontiers in Medicine

IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairm... more IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale.MethodsTo build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotyp...

npj Digital Medicine

Our study aims to identify children at risk of developing high myopia for timely assessment and i... more Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6–12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms – image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ −6.00 diopter) during teenage years (5 years later, age 11–17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93–0.95; Test dataset 0.91–0.93), clinical models (Prim...

Nanoscience & Nanotechnology Series

Eye and Vision, 2022

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ... more The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. ...

Journal of Clinical Ultrasound, 2022

The initial screening of medical images remains a fundamental bottleneck to the effective diagnos... more The initial screening of medical images remains a fundamental bottleneck to the effective diagnosis of time-sensitive diseases in many settings. This has been mitigated in recent years by the introduction of deep learning-based approaches, in many imaging fields. Perhaps one of the most popular applications has been toward replicating the judgment of human experts as closely as possible, with reference to some established scoring standard. This is the approach that Dong et al. have taken, in their study on classifying metacarpophalangeal (MCP) synovial proliferation (SP) in rheumatoid arthritis, from ultrasound images. The relevant scoring system is then the OMERACT-EULAR Synovitis Scoring (OESS) standard, which grades SP in a range of L0– L3. Area under receiver operating characteristic curve of around 0.90 were reported for the clinically relevant binary classification tasks of distinguishing between L0 and non-L0, and L0/L1 versus L2/L3.

Computer Vision – ACCV 2018 Workshops, 2019

As the application of deep learning (DL) advances in the healthcare sector, the need for simultan... more As the application of deep learning (DL) advances in the healthcare sector, the need for simultaneous, multi-annotated database of medical images for evaluations of novel DL systems grows. This study looked at DL algorithms that distinguish retinal images by the side of the eyes (Left and Right side) as well as the field positioning (Macular-centred or Optic Disc-centred) and evaluated these algorithms against a large dataset comprised of 7,953 images from multi-ethnic populations. For these convolutional neural networks, L/R model and Mac/OD model, a high AUC (0.978, 0.990), sensitivity (95.9%, 97.6%), specificity (95.5%, 96.7%) and accuracy (95.7%, 97.2%) were found, respectively, for the primary validation sets. The models presented high performance also using the external validation database.

2018 25th IEEE International Conference on Image Processing (ICIP), 2018

Vessel type classification is a preliminary step in quantifying the severity of various diseases.... more Vessel type classification is a preliminary step in quantifying the severity of various diseases. This paper proposes DBA, a simple yet effective vessel type classification method based on the principle that arteries are brighter than veins at the local scale. The weighted local difference of the red channel intensity of the main trunk of each vessel is compared with that of its two immediately neighbouring vessels, a feature that is highly correlated with vessel-rectified oxygen capacity, and in turn, vessel type. Experiments on the publicly-available INSPIRE-AVR and DRIVE datasets obtained average vessel accuracies of 0.9217/0.9071, and average pixel accuracies of 0.9602/0.9634 respectively, with particular effectiveness on images with low contrast, non-uniform illumination and colour variation confirmed on the SiMES 1 dataset.

Computer Vision – ACCV 2018 Workshops, 2019

End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal... more End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal image analysis tasks. However, such models’ generalizability to data from new sources may be less than optimal. We highlight some benefits of introducing intermediate goals in deep learning-based models.

Investigative Ophthalmology & Visual Science, 2018

Investigative Ophthalmology & Visual Science, 2019