Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.

Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learning classifiers for age-related macular degeneration (AMD). Artificial intelligence diagnostic algorithms can automatically detect and diagnose AMD through training data from large sets of fundus or OCT images. The use of AI algorithms is a powerful tool, and it is a method of obtaining a cost-effective, simple, and fast diagnosis of AMD.

METHODS

MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software and data on sensitivity, specificity and area under curve were extracted from the included studies. STATA 15.0 was used to conduct the meta-analysis.

RESULTS

Our search strategy identified 307 records from online databases and 174 records from gray literature. Total of 13 records, 64,798 subjects (and 612,429 images), were used for the quantitative analysis. The pooled estimate for sensitivity was 0.918 [95% CI: 0.678, 0.98] and specificity was 0.888 [95% CI: 0.578, 0.98] for AMD screening using machine learning classifiers. The relative odds of a positive screen test in AMD cases were 89.74 [95% CI: 3.05-2641.59] times more likely than a negative screen test in non-AMD cases. The positive likelihood ratio was 8.22 [95% CI: 1.52-44.48] and the negative likelihood ratio was 0.09 [95% CI: 0.02-0.52].

CONCLUSION

The included studies show promising results for the diagnostic accuracy of the machine learning classifiers for AMD and its implementation in clinical settings.

Role of Optical Coherence Tomography Imaging in Predicting Progression of Age-Related Macular Disease: A Survey

In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.

RAG-FW: A Hybrid Convolutional Framework for the Automated Extraction of Retinal Lesions and Lesion-Influenced Grading of Human Retinal Pathology

The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been rigorously tested on 43,613 scans from five highly complex publicly available datasets, containing multi-vendor scans, where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the accuracy of 98.70% for the correct severity grading of retinopathy.

3D imaging of proximal caries in posterior teeth using optical coherence tomography

Optical coherence tomography (OCT) can create cross-sectional images of tooth without X-ray exposure. This study aimed to investigate the diagnostic accuracy of 3D imaging of OCT for proximal caries in posterior teeth. Thirty-six human molar teeth with 51 proximal surfaces visibly 6 intact, 16 slightly demineralized, and 29 distinct carious changes were mounted to take digital radiographs and 3D OCT images. The sensitivity, specificity and area under the receiver operating characteristic curve (AUC) for the diagnosis of enamel caries and dentin caries were calculated to quantify the diagnostic ability of 3D OCT in comparison with digital radiography. Diagnostic accuracy was evaluated by the agreement with histology using weighted Kappa. OCT showed significantly higher sensitivity, AUC and Kappa values than radiography. OCT can be a safer option for the diagnosis of proximal caries in posterior teeth that can be applied to the patients without X-ray exposure.

Taxonomic Triangulation of Care in Healthcare Protocols: Mapping of Diagnostic Knowledge From Standardized Language

Taxonomic triangulation is a data mining technique for the management of care knowledge. This technique uses standardized languages, such as North American Nursing Diagnosis Association International, Nursing Outcomes Classification, and Nursing Interventions Classification, as well as logic. Its purpose is to find patterns in the data and identify care diagnoses. Triangulation can be applied to databases (clinical records) or to bibliographic sources (eg, protocols). The objective of this study is to identify the care diagnoses implicit in the nursing care protocols of the Community of Madrid. The method followed has three phases: knowledge extraction for mapping of variables, linking to diagnoses, and triangulation with analysis. The study analyzes six protocols, and 344 variables (167 assessment, 29 planning, and 148 intervention) and 6118 links have been extracted. Triangulation identified 165 NANDA diagnoses (68.48%), and only 25 labels were not revealed through this process. As a limitation, the results depend on the knowledge presented in protocols and change with language editions. Some labels included in the sample are recent and are not included in the links with nursing outcomes classification and nursing interventions classification. In conclusion, taxonomic triangulation makes it possible to manage knowledge, discover data patterns, and represent care situations.

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Objective : To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018.

Methods : Ovid MEDLINE ^® and Web of Science ^® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH ^® ) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook.

Results : Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies.

Conclusions : Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.