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10.1097/ICU.0000000000000693 http://scihub22266oqcxt.onion/10.1097/ICU.0000000000000693 32740069!ä!32740069 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Opin+Ophthalmol 2020 ; 31 (5): 357-365 Nephropedia Template TP {{tp|p=32740069|t=2020. Artificial intelligence for diabetic retinopathy screening, prediction and management |pdf=|usr=}}{{32740069}} gab.com Text Artificial intelligence for diabetic retinopathy screening, prediction and management JO: Curr Opin Ophthalmol http://www.kidney.de/pget.php?&tw=1&pmid=32740069 #FOAvip PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. RECENT FINDINGS: Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. SUMMARY: Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management. Twit Text FOAVip Artificial intelligence for diabetic retinopathy screening, prediction and management ????Curr Opin Ophthalmol http://www.kidney.de/pget.php?&tw=1&pmid=32740069 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32740069 #FOAvip English Wikipedia <ref>{{Cite pmid|32740069}}</ref> Artificial intelligence for diabetic retinopathy screening, prediction and management #MMPMID32740069 Gunasekeran DV; Ting DSW; Tan GSW; Wong TY Curr Opin Ophthalmol 2020[Sep]; 31 (5): 357-365 PMID32740069show ga PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. RECENT FINDINGS: Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. SUMMARY: Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management. |*Artificial Intelligence[MESH] |Cost-Benefit Analysis[MESH] |Diabetic Retinopathy/*diagnosis[MESH] |Health Services Accessibility[MESH] |Humans[MESH] |Ophthalmology/economics/trends[MESH]Artificial intelligence for diabetic retinopathy screening, prediction(epmc).pdf DeepDyve Pubget Overpricing