Pub Date : 2024-07-07DOI: 10.1101/2024.06.26.24308549
Ahlam Awad Mohammed, Aida Abashawl, Sarity Dodson, Wondu Alemayehu, Alemu Gemechu, Aemero Abateneh Mengesha, Dereje Kumsa, Tony Succar, Yineng Chen, Kathleen McWilliams, Vatinee Y. Bunya, Maureen G. Maguire, Matthew J Burton, Gui-shuang Ying, John H Kempen
Purpose: To report the design of FLuorometholone as Adjunctive MEdical Therapy for TT Surgery (FLAME) Trial. Design: Parallel design, double-masked, placebo-controlled clinical trial with 1:1 randomization to fluorometholone 0.1% eyedrops twice daily or placebo twice daily for four weeks in eyes undergoing trachomatous trichiasis (TT) surgery; assessing the efficacy, safety, and cost-effectiveness of fluorometholone 0.1% in preventing recurrent postoperative trichiasis.
{"title":"The FLuorometholone as Adjunctive MEdical Therapy for Trachomatous Trichiasis Surgery (FLAME) Trial: Study Design","authors":"Ahlam Awad Mohammed, Aida Abashawl, Sarity Dodson, Wondu Alemayehu, Alemu Gemechu, Aemero Abateneh Mengesha, Dereje Kumsa, Tony Succar, Yineng Chen, Kathleen McWilliams, Vatinee Y. Bunya, Maureen G. Maguire, Matthew J Burton, Gui-shuang Ying, John H Kempen","doi":"10.1101/2024.06.26.24308549","DOIUrl":"https://doi.org/10.1101/2024.06.26.24308549","url":null,"abstract":"Purpose: To report the design of FLuorometholone as Adjunctive MEdical Therapy for TT Surgery (FLAME) Trial. Design: Parallel design, double-masked, placebo-controlled clinical trial with 1:1 randomization to fluorometholone 0.1% eyedrops twice daily or placebo twice daily for four weeks in eyes undergoing trachomatous trichiasis (TT) surgery; assessing the efficacy, safety, and cost-effectiveness of fluorometholone 0.1% in preventing recurrent postoperative trichiasis.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background Trachoma is a leading infectious cause of blindness and of significant public health concern targeted for elimination. This study aimed to systematically summarize the magnitude of active trachoma among children aged 1-9 in Ethiopia from 2019-2024 Methods Database searches were conducted on Google Scholar, SCOPUS, PubMed, EMBASE, and AJOL for studies published between January 2019 to 31-March-2024 and with restriction to articles published only in English. Data extraction was done using a pre-prepared Excel sheet. STATA version 16 was used to perform the analysis. Heterogeneity between studies was assessed using I2 statistics and Cochrane Q. Qualitative synthesis was done to summarize the studies and random effect model was used to estimate the Pooled magnitude of active trachoma with a corresponding 95% confidence interval. Result A total of 17 studies with 19793 subjects were included in the meta-analysis. The pooled magnitude of active trachoma among children aged 1-9 years was found to be 18.39% (95% CI: 13.88, 22.90). We found a statistically significant heterogeneity between studies. Among the regions, Southwest region was found to have the highest magnitude (44.1%) (95%CI: 41.8%, 46.4%) and Dire Dawa was found to have the lowest (4.3%) (95%CI: 2.9%, 5.7%). Conclusion The magnitude of active trachoma is higher than the WHO target for elimination. There was significant interregional difference in magnitude of active trachoma. Strengthening SAFE strategy and health education and promotion is recommended.
{"title":"Active trachoma among children aged 1-9 years in Ethiopia: A meta-analysis from 2019 to 2024","authors":"Kibruyisfaw Weldeab Abore, Melat Tesfaye Asebot, Gifty Birhanemeskel Kebede, Asonya Abera Akuma, Mahlet Minwuyelet Dagne, Tewobesta Fesseha Tesfaye, Robel Tibebu Khasay, Estifanos Bekele Fole, Mahlet Tesfaye Abebe","doi":"10.1101/2024.07.01.24309760","DOIUrl":"https://doi.org/10.1101/2024.07.01.24309760","url":null,"abstract":"Background Trachoma is a leading infectious cause of blindness and of significant public health concern targeted for elimination. This study aimed to systematically summarize the magnitude of active trachoma among children aged 1-9 in Ethiopia from 2019-2024 Methods Database searches were conducted on Google Scholar, SCOPUS, PubMed, EMBASE, and AJOL for studies published between January 2019 to 31-March-2024 and with restriction to articles published only in English. Data extraction was done using a pre-prepared Excel sheet. STATA version 16 was used to perform the analysis. Heterogeneity between studies was assessed using I2 statistics and Cochrane Q. Qualitative synthesis was done to summarize the studies and random effect model was used to estimate the Pooled magnitude of active trachoma with a corresponding 95% confidence interval. Result A total of 17 studies with 19793 subjects were included in the meta-analysis. The pooled magnitude of active trachoma among children aged 1-9 years was found to be 18.39% (95% CI: 13.88, 22.90). We found a statistically significant heterogeneity between studies. Among the regions, Southwest region was found to have the highest magnitude (44.1%) (95%CI: 41.8%, 46.4%) and Dire Dawa was found to have the lowest (4.3%) (95%CI: 2.9%, 5.7%). Conclusion The magnitude of active trachoma is higher than the WHO target for elimination. There was significant interregional difference in magnitude of active trachoma. Strengthening SAFE strategy and health education and promotion is recommended.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141576414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1101/2024.07.01.24309679
Neslihan D Koseoglu, Jiangxia Wang, Frederick Anokye-Danso, Jose Amezcua Moreno, Eumee Cha, Franklin Fuchs, Jacqueline Teed, Jianing Yao, Yan Zhang, Rexford S Ahima, Mira M Sachdeva
Inner retinal thinning precedes clinical evidence of retinopathy in prediabetes and diabetes mellitus (DM), and may contribute to retinopathy development and progression. Serum levels of the adipokines leptin and adiponectin are inversely related in the setting of impaired glucose homeostasis, but their potential association with inner retinal thickness is unknown. In this prospective study, both eyes from 24 individuals with prediabetes or type 2 DM (glycated hemoglobin [HbA1c] ≥5.7) and 16 controls (HbA1c<5.7) underwent spectral-domain optical coherence tomography imaging of the macula, and thickness of the nerve fiber layer (NFL) and ganglion cell layer-inner plexiform layer (GCL-IPL) was analyzed in each subfield of the Early Treatment Diabetic Retinopathy Study grid. Serum samples were collected and metabolic factors, including adiponectin and leptin, were measured. Adjusted regression analyses revealed inverse associations of these adipokines with NFL thickness that did not differ between prediabetes/DM and controls, but differential positive associations of adiponectin with GCL-IPL thickness only in the prediabetes/DM group. The results of our pilot study suggest opposing roles for adiponectin and leptin in the retina, similar to their relationship in systemic disease, and suggest that serum adiponectin may represent a potential clinical biomarker for inner retinal thickness in patients with elevated HbA1c.
{"title":"Association of serum adiponectin and leptin levels with inner retinal thickness among individuals with or without elevated HbA1c","authors":"Neslihan D Koseoglu, Jiangxia Wang, Frederick Anokye-Danso, Jose Amezcua Moreno, Eumee Cha, Franklin Fuchs, Jacqueline Teed, Jianing Yao, Yan Zhang, Rexford S Ahima, Mira M Sachdeva","doi":"10.1101/2024.07.01.24309679","DOIUrl":"https://doi.org/10.1101/2024.07.01.24309679","url":null,"abstract":"Inner retinal thinning precedes clinical evidence of retinopathy in prediabetes and diabetes mellitus (DM), and may contribute to retinopathy development and progression. Serum levels of the adipokines leptin and adiponectin are inversely related in the setting of impaired glucose homeostasis, but their potential association with inner retinal thickness is unknown. In this prospective study, both eyes from 24 individuals with prediabetes or type 2 DM (glycated hemoglobin [HbA1c] ≥5.7) and 16 controls (HbA1c<5.7) underwent spectral-domain optical coherence tomography imaging of the macula, and thickness of the nerve fiber layer (NFL) and ganglion cell layer-inner plexiform layer (GCL-IPL) was analyzed in each subfield of the Early Treatment Diabetic Retinopathy Study grid. Serum samples were collected and metabolic factors, including adiponectin and leptin, were measured. Adjusted regression analyses revealed inverse associations of these adipokines with NFL thickness that did not differ between prediabetes/DM and controls, but differential positive associations of adiponectin with GCL-IPL thickness only in the prediabetes/DM group. The results of our pilot study suggest opposing roles for adiponectin and leptin in the retina, similar to their relationship in systemic disease, and suggest that serum adiponectin may represent a potential clinical biomarker for inner retinal thickness in patients with elevated HbA1c.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1101/2024.07.01.24309728
Vincenzo Rizzuto, Marzia Settino, Giacomo Stroffolini, Giuseppe Covello, Juris Vanags, Marta Naccarato, Roberto Montanari, Carlos Rocha de Lossada, Miguel Rechichi, Cosimo Mazzotta, Carlo Adornetto, Francesco Ricca, Gianluigi Greco, Guna Laganovska, Davide Borroni
Purpose: Purpose: The ocular surface (OS) microbiome is influenced by various factors and impacts ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. Methods: The 16S rRNA gene sequencing was performed on OS samples collected using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were iden- tified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Results: Nationality, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis showed higher microbial richness in Spanish subjects compared to Italian subjects and higher biodiversity in sports practitioners. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differ- ential abundance analysis identified several microbial genera associated with these PEL factors. ML approach confirmed the significance of nationality in classifying microbial profiles. Conclusion: This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.
目的：目的：眼表（OS）微生物组受各种因素影响，并对眼部健康产生影响。了解其组成和动态对于制定有针对性的眼部疾病干预措施至关重要。本研究旨在确定影响眼部微生物组组成的宿主变量，包括生理、环境和生活方式（PEL）因素，并建立眼部微生物组与健康结果之间的有效关联：方法：对使用 eSwab 采集的 OS 样品进行 16S rRNA 基因测序。提取 DNA，制备文库，纯化和分析 PCR 产物。确定了PEL混杂因素，并使用包括机器学习在内的各种生物信息学方法进行交叉验证，以确定微生物特征：结果：国籍、运动习惯和眼镜使用是影响眼部微生物组的重要 PEL 混杂因素。阿尔法多样性分析显示，西班牙受试者的微生物丰富度高于意大利受试者，体育从业者的生物多样性更高。Beta 多样性分析表明，微生物群落组成因国籍、年龄、运动和眼镜使用情况而存在显著差异。差异丰度分析确定了与这些 PEL 因素相关的几个微生物属。ML 方法证实了国籍在微生物特征分类中的重要性：本研究强调了在研究眼部微生物组时考虑 PEL 因素的重要性。我们的研究结果凸显了环境、生活方式和人口因素在塑造 OS 微生物组方面的复杂相互作用。未来的研究应进一步探索这些相互作用，以开发个性化的眼部健康管理方法。
{"title":"Ocular Surface Microbiome: Influences of Physiological, Environmental, and Lifestyle Factors","authors":"Vincenzo Rizzuto, Marzia Settino, Giacomo Stroffolini, Giuseppe Covello, Juris Vanags, Marta Naccarato, Roberto Montanari, Carlos Rocha de Lossada, Miguel Rechichi, Cosimo Mazzotta, Carlo Adornetto, Francesco Ricca, Gianluigi Greco, Guna Laganovska, Davide Borroni","doi":"10.1101/2024.07.01.24309728","DOIUrl":"https://doi.org/10.1101/2024.07.01.24309728","url":null,"abstract":"Purpose: Purpose: The ocular surface (OS) microbiome is influenced by various factors and impacts ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.\u0000Methods: The 16S rRNA gene sequencing was performed on OS samples collected using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were iden- tified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.\u0000Results: Nationality, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis showed higher microbial richness in Spanish subjects compared to Italian subjects and higher biodiversity in sports practitioners. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differ- ential abundance analysis identified several microbial genera associated with these PEL factors. ML approach confirmed the significance of nationality in classifying microbial profiles.\u0000Conclusion: This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-30DOI: 10.1101/2024.06.30.24309720
Martin Kallab, Sarah Hinterberger, Sophie Schneider, Olivia Murauer, Anna-Sophie Reisinger, Susanne Strohmaier, Alex S. Huang, Matthias Bolz, Clemens A. Strohmaier
Purpose To evaluate anterior segment optical coherence tomography (AS-OCT) parameters of bleb wall thickness (BWT) and total bleb height (TBH) in the early postoperative phase after Preserflo Microshunt (PM) implantation for their correlation to (a) surgical revision and (b) AS-OCT angiography (AS-OCTA) derived bleb vessel density (BVD). Methods A total of 23 patients with pharmacologically uncontrolled open angle glaucoma were studied. Post-operatively (at 1, 2, and 4 weeks and 2, 3, 6, 9, and 12 months) AS-OCT measurements of BWT/TBH and AS-OCTA measurements of BVD were acquired. Surgical revisions (needling or open revision) were recorded. Correlations of BWT and TBH to (a) need for surgical revision and to (b) BVD were assessed. Results In 10 of 23 patients, surgical revisions were performed 4 to 48 weeks after PM implantation. At 1, 2, and 4 weeks after surgery neither BWT nor TBH were significantly associated with future surgical revisions (BWT/TBH p-values: 1W 0.217/0.878, 2W 0.670/0.528, 4W 0.171/0.430). No correlations between BWT or TBH and BVD were found for any evaluated timepoint (1W, 2W, 4W). Conclusions Structural AS-OCT parameters were not predictive of the need for surgical revision after PM implantation. Consistent with this finding, these parameters were also not correlated with AS-OCTA derived BVD, which was shown to be a good biomarker for failure in a previous analysis. The discrepancy to similar studies after trabeculectomy may be due to bleb drainage differences between TE and PM. BVD seems to be a better predictor of surgical revision after PM implantation than BWT/TBH.
{"title":"No Correlation of Structural Anterior-Segment OCT Biomarkers with Bleb Vessel Density and Surgical Success after Preserflo Microshunt Implantation","authors":"Martin Kallab, Sarah Hinterberger, Sophie Schneider, Olivia Murauer, Anna-Sophie Reisinger, Susanne Strohmaier, Alex S. Huang, Matthias Bolz, Clemens A. Strohmaier","doi":"10.1101/2024.06.30.24309720","DOIUrl":"https://doi.org/10.1101/2024.06.30.24309720","url":null,"abstract":"Purpose\u0000To evaluate anterior segment optical coherence tomography (AS-OCT) parameters of bleb wall thickness (BWT) and total bleb height (TBH) in the early postoperative phase after Preserflo Microshunt (PM) implantation for their correlation to (a) surgical revision and (b) AS-OCT angiography (AS-OCTA) derived bleb vessel density (BVD). Methods\u0000A total of 23 patients with pharmacologically uncontrolled open angle glaucoma were studied. Post-operatively (at 1, 2, and 4 weeks and 2, 3, 6, 9, and 12 months) AS-OCT measurements of BWT/TBH and AS-OCTA measurements of BVD were acquired. Surgical revisions (needling or open revision) were recorded. Correlations of BWT and TBH to (a) need for surgical revision and to (b) BVD were assessed. Results\u0000In 10 of 23 patients, surgical revisions were performed 4 to 48 weeks after PM implantation. At 1, 2, and 4 weeks after surgery neither BWT nor TBH were significantly associated with future surgical revisions (BWT/TBH p-values: 1W 0.217/0.878, 2W 0.670/0.528, 4W 0.171/0.430). No correlations between BWT or TBH and BVD were found for any evaluated timepoint (1W, 2W, 4W). Conclusions\u0000Structural AS-OCT parameters were not predictive of the need for surgical revision after PM implantation. Consistent with this finding, these parameters were also not correlated with AS-OCTA derived BVD, which was shown to be a good biomarker for failure in a previous analysis. The discrepancy to similar studies after trabeculectomy may be due to bleb drainage differences between TE and PM. BVD seems to be a better predictor of surgical revision after PM implantation than BWT/TBH.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1101/2024.06.26.24309493
Noelle Stiles, Jeiran Choupan, Hossein Ameri, Vivek Patel, Yonggang Shi
The Argus II retinal prosthesis restores visual perception to late blind patients. It has been shown that structural changes occur in the brain due to late-onset blindness, including cortical thinning in visual regions of the brain. Following vision restoration, it is not yet known whether these visual regions are reinvigorated and regain a normal cortical thickness or retain the diminished thickness from blindness. We evaluated the cortical thicknesses of ten Argus II Retinal Prostheses patients, ten blind patients, and thirteen sighted participants. The Argus II patients on average had a thicker left Cuneus Cortex and Lateral Occipital Cortex relative to the blind patients. The duration of the Argus II use (time since implant in active users) significantly partially correlated with thicker visual cortical regions in the left hemisphere. Furthermore, in the two case studies (scanned before and after implantation), the patient with longer device use (44.5 months) had an increase in the cortical thickness of visual regions, whereas the shorter-using patient did not (6.5 months). Finally, a third case, scanned at three time points post-implantation, showed an increase in cortical thickness in the Lateral Occipital Cortex between 43.5 and 57 months, which was maintained even after 3 years of disuse (106 months). Overall, the Argus II patients cortical thickness was on average significantly rejuvenated in two higher visual regions and, patients using the implant for a longer duration had thicker visual regions. This research raises the possibility of structural plasticity reversing visual cortical atrophy in late-blind patients with prolonged vision restoration.
Argus II 视网膜假体能让晚期失明患者恢复视觉感知。研究表明，晚期失明会导致大脑结构发生变化，包括大脑视觉区域的皮质变薄。我们评估了十名 Argus II 视网膜假体患者、十名盲人患者和十三名视力正常者的大脑皮层厚度。与失明患者相比，Argus II 患者的左侧楔皮层和枕叶外侧皮层平均较厚。使用 Argus II 的持续时间（积极使用者的植入时间）与左半球视觉皮层区域的厚度有明显的部分相关性。此外，在两个病例研究（植入前后扫描）中，使用设备时间较长（44.5 个月）的患者视觉区域皮质厚度有所增加，而使用时间较短的患者（6.5 个月）则没有增加。最后，第三个病例在植入后的三个时间点进行了扫描，结果显示，在 43.5 个月至 57 个月期间，枕叶外侧皮层厚度有所增加，即使在停用 3 年（106 个月）后仍能保持。这项研究提出了结构可塑性逆转视力恢复期较长的晚盲患者视觉皮质萎缩的可能性。
{"title":"Visual Cortical Thickness Increases with Prolonged Artificial Vision Restoration","authors":"Noelle Stiles, Jeiran Choupan, Hossein Ameri, Vivek Patel, Yonggang Shi","doi":"10.1101/2024.06.26.24309493","DOIUrl":"https://doi.org/10.1101/2024.06.26.24309493","url":null,"abstract":"The Argus II retinal prosthesis restores visual perception to late blind patients. It has been shown that structural changes occur in the brain due to late-onset blindness, including cortical thinning in visual regions of the brain. Following vision restoration, it is not yet known whether these visual regions are reinvigorated and regain a normal cortical thickness or retain the diminished thickness from blindness.\u0000We evaluated the cortical thicknesses of ten Argus II Retinal Prostheses patients, ten blind patients, and thirteen sighted participants. The Argus II patients on average had a thicker left Cuneus Cortex and Lateral Occipital Cortex relative to the blind patients. The duration of the Argus II use (time since implant in active users) significantly partially correlated with thicker visual cortical regions in the left hemisphere. Furthermore, in the two case studies (scanned before and after implantation), the patient with longer device use (44.5 months) had an increase in the cortical thickness of visual regions, whereas the shorter-using patient did not (6.5 months). Finally, a third case, scanned at three time points post-implantation, showed an increase in cortical thickness in the Lateral Occipital Cortex between 43.5 and 57 months, which was maintained even after 3 years of disuse (106 months).\u0000Overall, the Argus II patients cortical thickness was on average significantly rejuvenated in two higher visual regions and, patients using the implant for a longer duration had thicker visual regions. This research raises the possibility of structural plasticity reversing visual cortical atrophy in late-blind patients with prolonged vision restoration.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1101/2024.06.27.24309574
Kerol R. Djoumessi Donteu, Ziwei Huang, Laura Kuehlewein, Annekatrin Rickmann, Natalia Simon, Lisa M. Koch, Philipp Berens
Background: Diabetic retinopathy (DR) is a frequent concomitant disease of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has been one of the first successful use cases for modern artificial intelligence in medicine. Current state-of-the-art systems typically use black-box models to make referral decisions, requiring post-hoc methods for AI-human interaction. Methods: In this retrospective reader study, we evaluated an inherently interpretable deep learning model, which explicitly models the local evidence of DR as part of its network architecture, for early DR screening. We trained the network on 34,350 high-quality fundus images from a publicly available dataset and validated its state-of-the-art performance on a large range of ten external datasets. We obtained detailed lesion annotations from ophthalmologists on 65 images to study if the class evidence maps highlight clinically relevant information. Finally, we tested the clinical usefulness of our model in a reader study, where we compared screening for DR without AI support to screening with AI support with and without AI explanations. Results: The inherently interpretable deep learning model obtained an accuracy of .906 [.900-.913] (95%-confidence interval) and an AUC of .904 [.894-.913] on the internal test set and similar performance on external datasets. High evidence regions directly extracted from the model contained clinically relevant lesions such as microaneurysms or haemorrhages with a high precision of .960 [.941 - .976]. Decision support by the model highlighting high-evidence regions in the image improved screening accuracy for difficult decisions and improved screening speed. Interpretation: Inherently interpretable deep learning models can reach state-of-the-art performance and support screening for early DR by improving human-AI collaboration.
背景：糖尿病视网膜病变（DR糖尿病视网膜病变（DR）是一种常见的糖尿病并发症，影响着全球数百万人。根据眼底图像筛查这种疾病是现代人工智能在医学领域的首批成功应用案例之一。目前最先进的系统通常使用黑盒模型来做出转诊决定，这需要人工智能与人类互动的事后方法：在这项回顾性读者研究中，我们评估了一种内在可解释的深度学习模型，该模型明确地将 DR 的局部证据作为其网络架构的一部分，用于 DR 的早期筛查。我们在一个公开数据集的 34,350 张高质量眼底图像上对该网络进行了训练，并在大量的十个外部数据集上验证了其最先进的性能。我们从眼科医生处获得了 65 幅图像的详细病变注释，以研究类证据图是否能突出显示临床相关信息。最后，我们在一项读者研究中测试了我们模型的临床实用性，在这项研究中，我们比较了没有人工智能支持的 DR 筛查和有人工智能支持的筛查，以及有人工智能解释和没有人工智能解释的筛查：结果：内在可解释深度学习模型在内部测试集上的准确率为 0.906 [.900-.913]（95% 置信区间），AUC 为 0.904 [.894-.913]，在外部数据集上的表现类似。从模型中直接提取的高证据区域包含微动脉瘤或出血等临床相关病变，精确度高达 0.960 [.941-.976]。通过模型突出图像中的高证据区域来提供决策支持，提高了疑难决策的筛查准确性，并提高了筛查速度：本质上可解释的深度学习模型可以达到最先进的性能，并通过改善人与人工智能的协作来支持早期 DR 的筛查。
{"title":"An Inherently Interpretable AI model improves Screening Speed and Accuracy for Early Diabetic Retinopathy","authors":"Kerol R. Djoumessi Donteu, Ziwei Huang, Laura Kuehlewein, Annekatrin Rickmann, Natalia Simon, Lisa M. Koch, Philipp Berens","doi":"10.1101/2024.06.27.24309574","DOIUrl":"https://doi.org/10.1101/2024.06.27.24309574","url":null,"abstract":"Background: Diabetic retinopathy (DR) is a frequent concomitant disease of diabetes, affecting millions worldwide. Screening for this disease based on fundus images has been one of the first successful use cases for modern artificial intelligence in medicine. Current state-of-the-art systems typically use black-box models to make referral decisions, requiring post-hoc methods for AI-human interaction.\u0000Methods: In this retrospective reader study, we evaluated an inherently interpretable deep learning model, which explicitly models the local evidence of DR as part of its network architecture, for early DR screening. We trained the network on 34,350 high-quality fundus images from a publicly available dataset and validated its state-of-the-art performance on a large range of ten external datasets. We obtained detailed lesion annotations from ophthalmologists on 65 images to study if the class evidence maps highlight clinically relevant information. Finally, we tested the clinical usefulness of our model in a reader study, where we compared screening for DR without AI support to screening with AI support with and without AI explanations.\u0000Results: The inherently interpretable deep learning model obtained an accuracy of .906 [.900-.913] (95%-confidence interval) and an AUC of .904 [.894-.913] on the internal test set and similar performance on external datasets. High evidence regions directly extracted from the model contained clinically relevant lesions such as microaneurysms or haemorrhages with a high precision of .960 [.941 - .976]. Decision support by the model highlighting high-evidence regions in the image improved screening accuracy for difficult decisions and improved screening speed.\u0000Interpretation: Inherently interpretable deep learning models can reach state-of-the-art performance and support screening for early DR by improving human-AI collaboration.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-24DOI: 10.1101/2024.06.24.24309385
Di Hu, junhong jiang, Qi Zhang, Zenan Lin, Mu-BioDig
Myopia is a leading cause of visual impairment worldwide, whose pathogenesis remains poorly understood. We comprehensively performed phenome-wide Mendelian randomization analysis (MR-PheWAS) to explore causal factors and potential therapeutic targets for myopia in participants from the UK Biobank study. The analysis revealed that 55 robust associations (1 disease, 2 employment, 3 cognitive functions, 4 sex-specific factor, 4 mental health, 5 lifestyle and environment, 10 sociodemographic, 12 physical measures and 14 ocular measures/conditions phenotypes) were significantly causally correlated with myopia. The results indicate that myopia may be influenced by several factors, such as serum metabolic traits, fatty acids intake, fat-related indexes, mental health, as well as some previously acknowledged risk factors. Future clinical trials are needed to verify our results.
{"title":"Exploring the potential causal effects of myopia: A phenome-wide Mendelian randomization association study","authors":"Di Hu, junhong jiang, Qi Zhang, Zenan Lin, Mu-BioDig","doi":"10.1101/2024.06.24.24309385","DOIUrl":"https://doi.org/10.1101/2024.06.24.24309385","url":null,"abstract":"Myopia is a leading cause of visual impairment worldwide, whose pathogenesis remains poorly understood. We comprehensively performed phenome-wide Mendelian randomization analysis (MR-PheWAS) to explore causal factors and potential therapeutic targets for myopia in participants from the UK Biobank study. The analysis revealed that 55 robust associations (1 disease, 2 employment, 3 cognitive functions, 4 sex-specific factor, 4 mental health, 5 lifestyle and environment, 10 sociodemographic, 12 physical measures and 14 ocular measures/conditions phenotypes) were significantly causally correlated with myopia. The results indicate that myopia may be influenced by several factors, such as serum metabolic traits, fatty acids intake, fat-related indexes, mental health, as well as some previously acknowledged risk factors. Future clinical trials are needed to verify our results.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-19DOI: 10.1101/2024.06.18.24309070
Walter Hauri-Rosales, Oswaldo Pérez, Marlon Garcia-Roa, Ellery López-Star, Ulises Olivares-Pinto
Vision loss due to chronic-degenerative diseases is a primary cause of blindness worldwide. Deep learning architectures utilizing optical coherence tomography images have proven effective for the early diagnosis of ocular pathologies. Nevertheless, most studies have emphasized the best outcomes using optimal hyperparameter combinations and extensive data availability. This focus has eclipsed the exploration of how model learning capacity varies with different data volumes. The current study evaluates the learning capabilities of efficient deep-learning classification models across various data amounts, aiming to determine the necessary data portion for effective clinical trial classifications of ocular pathologies. A comprehensive review was conducted, which included 295 papers that employed OCT images to classify one or more of the following retinal pathologies: Drusen, Diabetic Macular Edema, and Choroidal Neovascularization. Performance metrics and dataset details were extracted from these studies. Four Convolutional Neural Networks were selected and trained using three strategies: initializing with random weights, fine-tuning, and retraining only the classification layers. The resultant performance was compared based on training size and strategy to identify the optimal combination of model size, dataset size, and training approach. The findings revealed that, among the models trained with various strategies and data volumes, three achieved 99.9% accuracy, precision, recall, and F1 score. Two of these models were fine-tuned, and one used random weight initialization. Remarkably, two models reached 99% accuracy using only 10% of the original training dataset. Additionally, a model that was less than 10% the size of the others achieved 98.7% accuracy and an F1 score on the test set while requiring 100 times less computing time. This study is the first to assess the impact of training data size and model complexity on performance metrics across three scenarios: random weights initialization, fine-tuning, and retraining classification layers only, specifically utilizing optical coherence tomography images.
慢性退行性疾病导致的视力丧失是全球失明的主要原因。事实证明，利用光学相干断层扫描图像的深度学习架构对眼部病变的早期诊断非常有效。然而，大多数研究都强调利用最佳超参数组合和广泛的数据可用性来获得最佳结果。这一重点忽略了对模型学习能力如何随不同数据量而变化的探索。本研究评估了高效深度学习分类模型在不同数据量下的学习能力，旨在确定有效眼部病理临床试验分类所需的数据量。研究人员对 295 篇采用 OCT 图像对以下一种或多种视网膜病变进行分类的论文进行了全面审查：色素沉着、糖尿病黄斑水肿和脉络膜新生血管。从这些研究中提取了性能指标和数据集详情。我们选择了四个卷积神经网络，并采用三种策略对其进行训练：随机权重初始化、微调和仅对分类层进行再训练。根据训练规模和策略对结果性能进行了比较，以确定模型规模、数据集规模和训练方法的最佳组合。研究结果表明，在采用不同策略和数据量训练的模型中，有三个模型的准确率、精确率、召回率和 F1 分数都达到了 99.9%。其中两个模型进行了微调，一个使用了随机权重初始化。值得注意的是，有两个模型只使用了原始训练数据集的 10%，就达到了 99% 的准确率。此外，一个规模不到其他模型 10%的模型在测试集上达到了 98.7% 的准确率和 F1 分数，而所需的计算时间却减少了 100 倍。这项研究首次评估了训练数据大小和模型复杂度对三种情况下性能指标的影响：随机权重初始化、微调和仅重新训练分类层，特别是利用光学相干断层扫描图像。
{"title":"Optimizing Ocular Pathology Classification with CNNs and OCT Imaging: A Systematic and Performance Review","authors":"Walter Hauri-Rosales, Oswaldo Pérez, Marlon Garcia-Roa, Ellery López-Star, Ulises Olivares-Pinto","doi":"10.1101/2024.06.18.24309070","DOIUrl":"https://doi.org/10.1101/2024.06.18.24309070","url":null,"abstract":"Vision loss due to chronic-degenerative diseases is a primary cause of blindness worldwide. Deep learning architectures utilizing optical coherence tomography images have proven effective for the early diagnosis of ocular pathologies. Nevertheless, most studies have emphasized the best outcomes using optimal hyperparameter combinations and extensive data availability. This focus has eclipsed the exploration of how model learning capacity varies with different data volumes. The current study evaluates the learning capabilities of efficient deep-learning classification models across various data amounts, aiming to determine the necessary data portion for effective clinical trial classifications of ocular pathologies. A comprehensive review was conducted, which included 295 papers that employed OCT images to classify one or more of the following retinal pathologies: Drusen, Diabetic Macular Edema, and Choroidal Neovascularization. Performance metrics and dataset details were extracted from these studies. Four Convolutional Neural Networks were selected and trained using three strategies: initializing with random weights, fine-tuning, and retraining only the classification layers. The resultant performance was compared based on training size and strategy to identify the optimal combination of model size, dataset size, and training approach. The findings revealed that, among the models trained with various strategies and data volumes, three achieved 99.9% accuracy, precision, recall, and F1 score. Two of these models were fine-tuned, and one used random weight initialization. Remarkably, two models reached 99% accuracy using only 10% of the original training dataset. Additionally, a model that was less than 10% the size of the others achieved 98.7% accuracy and an F1 score on the test set while requiring 100 times less computing time. This study is the first to assess the impact of training data size and model complexity on performance metrics across three scenarios: random weights initialization, fine-tuning, and retraining classification layers only, specifically utilizing optical coherence tomography images.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-28DOI: 10.1101/2024.05.26.24307941
Julia E Jaeger, Matthew Burke, Duoduo Wu, Emily Ern-Min Curren, Sandric Chee Yew Leong, Robert Symons, Blanche Xiao Hong Lim, Xinyi Su, Jodhbir Singh Mehta, Andri Kartasasmita Riau, Chris Hong Long Lim
Purpose There is increasing evidence that microplastics exert harmful effects on human health and on the ocular surface. Recent studies have highlighted the presence of ocular surface exposure to microplastics via shedding from contact lenses. This study aims to investigate if microplastic particles are present in commonly used eyedrops in single-use plastic vials.
{"title":"Microplastics Identified in Commercial Over-the-counter Lubricant Eyedrops","authors":"Julia E Jaeger, Matthew Burke, Duoduo Wu, Emily Ern-Min Curren, Sandric Chee Yew Leong, Robert Symons, Blanche Xiao Hong Lim, Xinyi Su, Jodhbir Singh Mehta, Andri Kartasasmita Riau, Chris Hong Long Lim","doi":"10.1101/2024.05.26.24307941","DOIUrl":"https://doi.org/10.1101/2024.05.26.24307941","url":null,"abstract":"<strong>Purpose</strong> There is increasing evidence that microplastics exert harmful effects on human health and on the ocular surface. Recent studies have highlighted the presence of ocular surface exposure to microplastics via shedding from contact lenses. This study aims to investigate if microplastic particles are present in commonly used eyedrops in single-use plastic vials.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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