Understanding and Improving the Performance of Web Page Loads

The web is vital to our daily lives, yet web pages are often slow to load. The inefficiency and complexity of loading web pages can be attributed to the dependencies between resources within a web page, which also leads to underutilization of the CPU and network on client devices. My thesis research seeks solutions that enable better use of the client-side CPU and network during page loads. Such solutions can be categorized into three types of approaches: 1) leveraging a proxy to optimize web page loads, 2) modifying the end-to-end interaction between client browsers and web servers, and 3) rewriting web pages. Each approach offers various benefits and trade-offs. This dissertation explores three specific solutions. First, CASPR is a proxy-based solution that enables clients to offload JavaScript computations to proxies. CASPR loads web pages on behalf of clients and transforms every page into a version that is simpler for clients to process, leading to a 1.7s median improvement in web page rendering for popular CASPR web pages. Second, Vroom rethinks how page loads work; in order to minimize dependencies between resources, it enables web servers to provide resource hints to clients and ensures that resources are loaded with proper prioritization. As a result, Vroom halves the median load times for popular news and sports websites. Finally, I conducted a longitudinal study to understand how web pages have changed over time and how these changes have affected performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163157/1/vaspol_1.pd

Patient and provider perspectives on barriers to screening for Diabetic Retinopathy: An 3 exploratory study from Southern India

Objective: Diabetic retinopathy is one of the leading causes of visual impairment after cataract and uncorrected refractive error. It has major public health implications globally, especially in countries such as India where the prevalence of diabetes is high. With timely screening and intervention, the disease progression to blindness can be prevented, but several barriers exist. As compliance to diabetic retinopathy screening in people with diabetes is very poor in India, this study was conducted to explore understanding of and barriers to diabetic retinopathy screening from the perspectives of patients and healthcare providers. Methods: Using qualitative methods, 15 consenting adult patients with diabetes were selected purposively from those attending a large tertiary care private eye hospital in southern India. Eight semistructured interviews were carried out with healthcare providers working in large private hospitals. All interviews were audiotaped, transcribed verbatim and analysed using the framework analytical approach. Results: Four themes that best explained the data were recognising and living with diabetes, care-seeking practices, awareness about diabetic retinopathy and barriers to diabetic retinopathy screening. Findings showed that patients were aware of diabetes but understanding of diabetic retinopathy and its complications was poor. Absence of symptoms, difficulties in doctor–patient interactions and tedious nature of follow-up care were some major deterrents to care seeking reported by patients. Difficulties in communicating information about diabetic retinopathy to less literate patients, heavy work pressure and silent progression of the disease were major barriers to patients coming for follow-up care as reported by healthcare providers. Conclusions: Enhancing patient understanding through friendly doctor–patient interactions will promote trust in the doctor. The use of an integrated treatment approach including education by counsellors, setting up of patient support groups, telescreening approaches and use of conversation maps may prove more effective in the long run

Diagnosis of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. Results: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validatedwith56PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. Conclusions: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. Translational Relevance: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis. Introductio

Real-world safety of intravitreal bevacizumab and ranibizumab treatments for retinal diseases in Thailand: a prospective observational study

Background: There is very limited evidence examining serious systemic adverse events (SSAEs) and post-injection endophthalmitis of intravitreal bevacizumab (IVB) and intravitreal ranibizumab (IVR) treatments in Thailand and low- and middle-income countries. Moreover, findings from the existing trials might have limited generalizability to certain populations and rare SSAEs. Objectives: This prospective observational study aimed to assess and compare the safety profiles of IVB and IVR in patients with retinal diseases in Thailand. Methods: Between 2013 and 2015, 6354 patients eligible for IVB or IVR were recruited from eight hospitals. Main outcomes measures were prevalence and risk of SSAEs, mortality, and endophthalmitis during the 6-month follow-up period. Results: In the IVB and IVR groups, 94 and 6% of patients participated, respectively. The rates of outcomes in the IVB group were slightly greater than in the IVR group. All-cause mortality rates in the IVB and IVR groups were 1.10 and 0.53%, respectively. Prevalence rates of endophthalmitis and non-fatal strokes in the IVB group were 0.04% of 16,421 injections and 0.27% of 5975 patients, respectively, whereas none of these events were identified in the IVR group. There were no differences between the two groups in the risks of mortality, arteriothrombotic events (ATE), and non-fatal heart failure (HF). Adjustment for potential confounding factors and selection bias using multivariable models for time-to-event outcomes and propensity scores did not alter the results. Conclusions: The rates of SAEs in both groups were low. The IVB and IVR treatments were not associated with significant risks of mortality, ATE, and non-fatal HF. Trial Registration: Thai Clinical Trial Registry identifier TCTR20141002001

Diagnosis of Polypoidal Choroidal Vasculopathy from Fluorescein Angiography Using Deep Learning

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. Results: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image-level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validated with 56 PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. Conclusions: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. Translational Relevance: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81–0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85–0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging

Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT). DESIGN: Retrospective validation of a DLS across international datasets. PARTICIPANTS: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the UK with mild DR and suspicion of DME based on CFP. METHODS: The DLS was trained using DME labels from OCT. Presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared to expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated integration of the current DLS into an algorithm trained to detect DR from CFPs. MAIN OUTCOME MEASURES: Superiority of specificity and non-inferiority of sensitivity of the DLS for the detection of center-involving DME, using device specific thresholds, compared to experts. RESULTS: Primary analysis in a combined dataset spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity compared to expert graders who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (p=0.008) and non-inferior sensitivity (p 50%) and a sensitivity of 100% (p=0.02 for sensitivity > 90%). CONCLUSIONS: The DLS can generalize to multiple international populations with an accuracy exceeding experts. The clinical value of this DLS to reduce false positive referrals, thus decreasing the burden on specialist eye care, warrants prospective evaluation

International Classification System for Ocular Complications of Anti-VEGF Agents in Clinical Trials

\ua9 2024 American Academy of OphthalmologyPurpose: Complications associated with intravitreal anti-VEGF therapies are reported inconsistently in the literature, thus limiting an accurate evaluation and comparison of safety between studies. This study aimed to develop a standardized classification system for anti-VEGF ocular complications using the Delphi consensus process. Design: Systematic review and Delphi consensus process. Participants: Twenty-five international retinal specialists participated in the Delphi consensus survey. Methods: A systematic literature search was conducted to identify complications of intravitreal anti-VEGF agent administration based on randomized controlled trials (RCTs) of anti-VEGF therapy. A comprehensive list of complications was derived from these studies, and this list was subjected to iterative Delphi consensus surveys involving international retinal specialists who voted on inclusion, exclusion, rephrasing, and addition of complications. Furthermore, surveys determined specifiers for the selected complications. This iterative process helped to refine the final classification system. Main Outcome Measures: The proportion of retinal specialists who choose to include or exclude complications associated with anti-VEGF administration. Results: After screening 18 229 articles, 130 complications were categorized from 145 included RCTs. Participant consensus via the Delphi method resulted in the inclusion of 91 complications (70%) after 3 rounds. After incorporating further modifications made based on participant suggestions, such as rewording certain phrases and combining similar terms, 24 redundant complications were removed, leaving a total of 67 complications (52%) in the final list. A total of 14 complications (11%) met exclusion thresholds and were eliminated by participants across both rounds. All other remaining complications not meeting inclusion or exclusion thresholds also were excluded from the final classification system after the Delphi process terminated. In addition, 47 of 75 proposed complication specifiers (63%) were included based on participant agreement. Conclusions: Using the Delphi consensus process, a comprehensive, standardized classification system consisting of 67 ocular complications and 47 unique specifiers was established for intravitreal anti-VEGF agents in clinical trials. The adoption of this system in future trials could improve consistency and quality of adverse event reporting, potentially facilitating more accurate risk-benefit analyses. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article