標題: Titlebook: Artificial Intelligence in Ophthalmology; Andrzej Grzybowski Book 2021 Springer Nature Switzerland AG 2021 Automatic software.Deep learnin [打印本頁] 作者: 即將過時 時間: 2025-3-21 18:59
書目名稱Artificial Intelligence in Ophthalmology影響因子(影響力)
書目名稱Artificial Intelligence in Ophthalmology影響因子(影響力)學科排名
書目名稱Artificial Intelligence in Ophthalmology網絡公開度
書目名稱Artificial Intelligence in Ophthalmology網絡公開度學科排名
書目名稱Artificial Intelligence in Ophthalmology被引頻次
書目名稱Artificial Intelligence in Ophthalmology被引頻次學科排名
書目名稱Artificial Intelligence in Ophthalmology年度引用
書目名稱Artificial Intelligence in Ophthalmology年度引用學科排名
書目名稱Artificial Intelligence in Ophthalmology讀者反饋
書目名稱Artificial Intelligence in Ophthalmology讀者反饋學科排名
作者: 領袖氣質 時間: 2025-3-21 20:47 作者: Obsessed 時間: 2025-3-22 03:48
https://doi.org/10.1007/978-3-642-38944-3l data, a prerequisite to translating the techniques to patient care, is often times lacking or unsuccessful. Despite this, the potential of AI as a diagnostic and screening tool to improve patient care highlights the continued need for experimental approaches to be applied to tasks which have the p作者: projectile 時間: 2025-3-22 05:10
Nach dem Crew Resource Managementwith DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms作者: 鞏固 時間: 2025-3-22 09:51
Nach dem Crew Resource Managementmic risk factors, highlighting the importance of controlling these to prevent visual impairment. Compared to 17 human assessors, SELENA performed with comparable accuracy and spent significantly less time. SELENA also demonstrated the potential of using DL systems in developing countries. In Zambia 作者: abracadabra 時間: 2025-3-22 14:12 作者: Acclaim 時間: 2025-3-22 18:10
Technical Aspects of Deep Learning in Ophthalmology,nal neural networks replace dense matrix multiplication in neural networks with convolution and pooling operations to resemble the biological process of animal visual cortex. Recurrent operations in recurrent neural networks allow models to store past history. Finally, we provide a brief introductio作者: STEER 時間: 2025-3-22 22:36
Experimental Artificial Intelligence Systems in Ophthalmology: An Overview,l data, a prerequisite to translating the techniques to patient care, is often times lacking or unsuccessful. Despite this, the potential of AI as a diagnostic and screening tool to improve patient care highlights the continued need for experimental approaches to be applied to tasks which have the p作者: Ejaculate 時間: 2025-3-23 01:39
AI and Glaucoma,with DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms作者: 抵消 時間: 2025-3-23 06:10 作者: Inscrutable 時間: 2025-3-23 12:53 作者: GRAZE 時間: 2025-3-23 17:18
Artificial Intelligence in Ophthalmology: Promises, Hazards and Challenges, for future medicine. It also presents the increase in published scientific studies using artificial intelligence in medicine in last decade..The regulation frameworks for medical devices, including AI medical devices, in the USA and in the European Union is discussed. Moreover, the problem of acces作者: quiet-sleep 時間: 2025-3-23 21:42 作者: monochromatic 時間: 2025-3-24 01:29 作者: Inordinate 時間: 2025-3-24 02:28 作者: Jogging 時間: 2025-3-24 08:25 作者: 避開 時間: 2025-3-24 12:32 作者: 燈絲 時間: 2025-3-24 15:31
Experimental Artificial Intelligence Systems in Ophthalmology: An Overview,ata for the purposes of disease detection, classification and prediction, as well as surgical screening, training and robotics. Automatic image processing and feature extraction from multimodal sources such as visual fields, optical coherence tomography and fundus photos can be combined with patient作者: 壓迫 時間: 2025-3-24 21:06
Artificial Intelligence in Age-Related Macular Degeneration (AMD),classified into early, intermediate, and late stages. In some patients, AMD advances to the late vision-threatening stage slowly; in others, the disease progresses faster and may quickly lead to a loss of vision in one or both eyes. There is, therefore, a critical need to detect AMD severity accurat作者: gruelling 時間: 2025-3-24 23:33
AI and Glaucoma,nlike diabetic retinopathy and age-related macular degeneration where early DL initiatives in ophthalmology focused on, there have been limited but expanding efforts for utilizing DL algorithms to improve diagnosis and management of glaucoma. In this chapter, we provide a summary of current AI appli作者: arcane 時間: 2025-3-25 04:58 作者: obsolete 時間: 2025-3-25 08:02 作者: HILAR 時間: 2025-3-25 14:01
Google and DeepMind: Deep Learning Systems in Ophthalmology,y, artificial intelligence studies have spanned a diverse spectrum including algorithm development, human computer interaction, clinical validation, and novel biomarker discovery. In this chapter we highlight the work of Google and DeepMind in these areas, as a set of end-to-end case studies for dev作者: accrete 時間: 2025-3-25 16:38
Singapore Eye Lesions Analyzer (SELENA): The Deep Learning System for Retinal Diseases,cular degeneration (AMD), retinopathy of prematurity and systemic cardiovascular diseases. In 2017, an international team of physicians and computer scientists reported that the Singapore Eye Lesion Analyzer (SELENA), a DL system, possessed excellent diagnostic performance for three major blinding c作者: thrombus 時間: 2025-3-25 21:04 作者: Macronutrients 時間: 2025-3-26 04:04 作者: COLON 時間: 2025-3-26 06:15
Artificial Intelligence for Cataract Management,duce healthcare burdens. However, imbalanced distribution of medical resources has limited the coverage of cataract management. Besides, the current standard of management of cataract patients still needs improvement for better visual outcomes. With the advent of artificial intelligence (AI) technol作者: coalition 時間: 2025-3-26 09:16 作者: 危機 時間: 2025-3-26 13:58
Artificial Intelligence in Cataract Surgery Training,o the reduction of up to 41,846 readmissions and save $620.3 million per year. It has been established that poor technical skill is associated with an increased risk of severe adverse events after surgery, and traditional models to train surgeons are being challenged by rapid advances in technology,作者: Obligatory 時間: 2025-3-26 19:38
Artificial Intelligence in Ophthalmology Triaging, are both time consuming and prone to human error. The use of deep learning (DL) and natural language processing (NLP) in ophthalmology triaging is a novel application of artificial intelligence (AI) established at the South Australian Institute of Ophthalmology (SAIO), Australia. AI assisted triagi作者: 藕床生厭倦 時間: 2025-3-26 22:15
https://doi.org/10.1007/978-3-642-38944-3 preprocessing fundus retinal images prior to applying the inference process which may support the diagnosis of many diseases. Finally, we present a sample technique for diagnosing diabetic retinopathy.作者: Flawless 時間: 2025-3-27 04:23 作者: NUDGE 時間: 2025-3-27 05:28
Selected Image Analysis Methods for Ophthalmology, preprocessing fundus retinal images prior to applying the inference process which may support the diagnosis of many diseases. Finally, we present a sample technique for diagnosing diabetic retinopathy.作者: colostrum 時間: 2025-3-27 12:16 作者: parallelism 時間: 2025-3-27 13:48
https://doi.org/10.1007/978-3-642-38944-3to fundus photographs for AMD detection and classification; (3) highlight the potential utility of deep learning methodologies to assist and enhance clinical decision-making in patients with AMD; and (4) provide a starting point for researchers who are interested in this field by providing useful resources such as code and data.作者: nonchalance 時間: 2025-3-27 21:33
Vor dem Crew Resource Management learning systems (DLS) was based on the annotations (gradings) by trained graders at the University of Wisconsin Fundus Photograph Reading Center, which is the designated reading center for the AREDS.作者: 無能性 時間: 2025-3-28 00:38
Chlorination By-Products of Anticancer Drugsocedure to mastery is consequential. This chapter discusses the current methods available for evaluating technical skill in cataract surgery as well as the recent technological advancements that have enabled the capture and analysis of large amounts of complex surgical data for a more automated assessment of objective skills.作者: Metamorphosis 時間: 2025-3-28 04:44
Artificial Intelligence in Age-Related Macular Degeneration (AMD),to fundus photographs for AMD detection and classification; (3) highlight the potential utility of deep learning methodologies to assist and enhance clinical decision-making in patients with AMD; and (4) provide a starting point for researchers who are interested in this field by providing useful resources such as code and data.作者: Apraxia 時間: 2025-3-28 09:47 作者: 輕信 時間: 2025-3-28 13:39
Artificial Intelligence in Cataract Surgery Training,ocedure to mastery is consequential. This chapter discusses the current methods available for evaluating technical skill in cataract surgery as well as the recent technological advancements that have enabled the capture and analysis of large amounts of complex surgical data for a more automated assessment of objective skills.作者: 虛假 時間: 2025-3-28 16:03 作者: 隱語 時間: 2025-3-28 21:57 作者: phase-2-enzyme 時間: 2025-3-28 23:58 作者: Anguish 時間: 2025-3-29 04:00
Nach dem Crew Resource Managementon surgery (particularly in custom planning of the procedure), and prevention of iatrogenic ectasias. This area of medicine will continue to grow in complexity along with new technology promising to make its application routine in the future.作者: Amnesty 時間: 2025-3-29 10:28 作者: 占卜者 時間: 2025-3-29 14:58 作者: 終止 時間: 2025-3-29 18:47 作者: clarify 時間: 2025-3-29 22:46
Artificial Intelligence in Diabetic Retinopathy,g countries. Diabetes can cause a number of significant complications, each of them associated with significant morbidity, requiring different, highly qualified medical personnel to diagnose and treat them. This poses a challenge for the local health services which often struggle with either delivering or funding the appropriate care.作者: Ornithologist 時間: 2025-3-30 02:38 作者: Obstruction 時間: 2025-3-30 07:58
Artificial Intelligence for Cataract Management,ders, cataracts hold promise for the management by AI agents considering their apparently uniform lesion areas and pathological bases. In the following section, we summarize the state-of-the-art AI research in each clinical scenario for cataract patients and give the prospects about AI-based cataract management.作者: 不適當 時間: 2025-3-30 09:48
Artificial Intelligence in Ophthalmology Triaging,1%. Technical challenges in AI assisted triaging include small dataset size, distant labels and the presence of specialized medical vocabulary. Future research relating to AI assisted triaging should endeavour to use larger sample sizes, specialist guided triage allocation, and data from multiple centres.作者: 道學氣 時間: 2025-3-30 13:33 作者: vasculitis 時間: 2025-3-30 18:30 作者: Incompetent 時間: 2025-3-30 22:54 作者: Free-Radical 時間: 2025-3-31 02:26
V. M.-Y. Lee,J. Q. Trojanowski,Y. Christenical liability [1]. Autonomous AI systems are thus different from Assistive AI sys-tems, which help a clinician make better diagnos-tic or management decisions, and where the liability for the medical decision remains with the clinician [2].作者: itinerary 時間: 2025-3-31 05:52
https://doi.org/10.1007/978-3-662-55484-5nd novel biomarker discovery. In this chapter we highlight the work of Google and DeepMind in these areas, as a set of end-to-end case studies for developing and implementing artificial intelligence in clinical practice.作者: Sciatica 時間: 2025-3-31 12:09
Basics of Artificial Intelligence for Ophthalmologists,t much insight into how these results were accomplished. This chapter intends to comprehensively elucidate the basic principles of Artificial Intelligence to help ophthalmologists get a feel for the strengths and limits of the techniques, as well as how they may use them in clinical practice.作者: 危機 時間: 2025-3-31 15:06 作者: Accrue 時間: 2025-3-31 20:49
