Clinical research in dementia: A perspective on implementing innovation.

The increasing global prevalence of dementia demands concrete actions that are aimed strategically at optimizing processes that drive clinical innovation. The first step in this direction requires outlining hurdles in the transition from research to practice. The different parties needed to support translational processes have communication mismatches; methodological gaps hamper evidence-based decision-making; and data are insufficient to provide reliable estimates of long-term health benefits and costs in decisional models. Pilot projects are tackling some of these gaps, but appropriate methods often still need to be devised or adapted to the dementia field. A consistent implementation perspective along the whole translational continuum, explicitly defined and shared among the relevant stakeholders, should overcome the "research-versus-adoption" dichotomy, and tackle the implementation cliff early on. Concrete next steps may consist of providing tools that support the effective participation of heterogeneous stakeholders and agreeing on a definition of clinical significance that facilitates the selection of proper outcome measures.

Semiquantitative Criteria in the Eye Bank That Correlate with Cornea Guttata in Donor Corneas.

BACKGROUND: Cornea guttata may not be recognized in the eye bank and recent studies have displayed that guttae are transplanted in about 15% of cases in varying severities. The purpose of this study was to establish semiquantitative criteria for the detection of cornea guttata in donor corneas in the eye bank. METHODS: In this retrospective cohort study, preoperative endothelial pictures of donor corneas were collected and classified according to the post-penetrating keratoplasty cornea guttata grade into three distinct groups: group 1 consists of healthy corneas with no guttae (guttata grade 0); group 2 constitutes corneas with mild asymptomatic cornea guttata (guttata grade +); and group 3 comprises corneas with advanced widespread cornea guttata (guttata grade ++/+++/++++). The preoperative pictures of each group were then individually analyzed using the following five semiquantitative criteria: The number and the area of the cell-depleted surfaces, the presence of less than 50% of the cells having a hexagonal or a circular shape, the presence of cell membrane defects and interruptions, the presence of blebs in the cell membrane, and the presence of groups of cells with a distinct whitish color. RESULTS: In total, 262 patients were included in this study, with a total number of 1582 preoperative donor corneal endothelial pictures. Out of those pictures, groups 1, 2, and 3 encompassed 995 (62.9%), 411 (26.0%), and 176 (11.1%) pictures, respectively. Three out of the five eye bank criteria were found to correlate with postoperative cornea guttata with a highly significant p value of < 0.001. These three criteria are the presence of less than 50% of the cells having a hexagonal or a circular shape, the presence of cell membrane defects and interruptions and, the presence of blebs. The presence of groups of cells with a distinct whitish color was only a weak predictive factor for cornea guttata (p = 0.069). There was no statistically significant correlation between the number and the area of cell-depleted surfaces and postoperative cornea guttata with a p = 0.181. CONCLUSION: Three semiquantitative criteria that can be detected in the eye bank using inverted light microscopy seem to correlate with postoperative cornea guttata: The presence of blebs, the presence of cell membrane defects and interruptions, as well as endothelial pictures with less than 50% of the cells having a hexagonal of circular shape. The presence of groups of cells with a distinct whitish color appears to be a weak predictor of cornea guttata.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task.

BACKGROUND: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. METHODS: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. RESULTS: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). CONCLUSION: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

P06-A143†An artificial-intelligence-based decision support tool for the detection of Cornea guttata on the donor corneas in the eye bank.

PURPOSE: Cornea guttata (CG) prevalence post keratoplasty varies from 15 to 18%, with 1 to 2% of the cases presenting with significant negative outcomes. The purpose of this research project is to create a program based on artificial intelligence (AI) that helps with the detection of CG in the donor corneas (DC) in the eye bank. METHODS: Preoperative corneal endothelial images (PCEI) of patients who underwent keratoplasty were collected and classified into 2 groups according to the postoperative CG grade. Group 1 included healthy corneas and those having mild postoperative CG, while group 2 included corneas with severe postoperative CG. Using previously tested semi-quantitative morphological criteria along with other characteristics such as donor age and lens status, the PCEI were analyzed and used to create and train an AI-based tool for the detection of CG. The underlying concept of the tool compares previous cases with comparable properties to the DC in test. The postoperative CG grades of previous cases similar to the DC in test determine the prediction for its CG grade. Finally, the features and CG grade of the analyzed DC are stored in the database for future use. RESULTS: In total, 6221 PCEI belonging to 1078 patients were used to create a transparent and explainable decision support tool for the detection of CG through a hybrid approach combining 2 components. (1) Graphical analytic tools, whereby the PCEI pass multiple OpenCV-based image processing steps including the Watershed transform algorithm. In this step, cell membranes are delineated, and abnormally large cells or cell depleted areas are marked in red. Several other cell representations such as 'honeycomb' representation are created for an enhanced visualization of the endothelial layer (EL). (2) Machine learning (ML) classifiers including Case-Based Reasoning were created to detect CG. Initial experiments showed a performance comparable to humans (4-fold evaluation yielded precision: weighted F1 score:0.93). CONCLUSION: We presented an AI-based program able to facilitate the detection of CG in the DC in the eye bank by comparing the PCEIs with relevant previous cases, using ML classifiers and offering an enhanced visualization of the EL. The evaluation and optimization of this program will follow as the next stage of our project.

Detecting the corneal neovascularisation area using artificial intelligence.

AIMS: To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area. METHODS: Slit lamp images of patients with CoNV were exported from the electronic medical records and included in the study. An experienced ophthalmologist made manual annotations of the CoNV areas, which were then used to create, train and evaluate an automated image analysis tool that uses deep learning to segment and detect CoNV areas. A pretrained neural network (U-Net) was used and fine-tuned on the annotated images. Sixfold cross-validation was used to evaluate the performance of the algorithm on each subset of 20 images. The main metric for our evaluation was intersection over union (IoU). RESULTS: The slit lamp images of 120 eyes of 120 patients with CoNV were included in the analysis. Detections of the total corneal area achieved IoU between 90.0% and 95.5% in each fold and those of the non-vascularised area achieved IoU between 76.6% and 82.2%. The specificity for the detection was between 96.4% and 98.6% for the total corneal area and 96.6% and 98.0% for the non-vascularised area. CONCLUSION: The proposed algorithm showed a high accuracy compared with the measurement made by an ophthalmologist. The study suggests that an automated tool using artificial intelligence may be used for the calculation of the CoNV area from the slit-lamp images of patients with CoNV.

Exploitation vs. exploration-computational temporal and semantic analysis explains semantic verbal fluency impairment in Alzheimer's disease.

Impaired Semantic Verbal Fluency (SVF) in dementia due to Alzheimer's Disease (AD) and its precursor Mild Cognitive Impairment (MCI) is well known. Yet, it remains open whether this impairment mirrors the breakdown of semantic memory retrieval processes or executive control processes. Therefore, qualitative analysis of the SVF has been proposed but is limited in terms of methodology and feasibility in clinical practice. Consequently, research draws no conclusive picture which of these afore-mentioned processes drives the SVF impairment in AD and MCI. This study uses a qualitative computational approach-combining temporal and semantic information-to investigate exploitation and exploration patterns as indicators for semantic memory retrieval and executive control processes. Audio SVF recordings of 20 controls (C, 66-81 years), 55 MCI (57-94 years) and 20 AD subjects (66-82 years) were assessed while groups were matched according to age and education. All groups produced, on average, the same amount of semantically related items in rapid succession within word clusters. Conversely, towards AD, there was a clear decline in semantic as well as temporal exploration patterns between clusters. Results strongly point towards preserved exploitation-semantic memory retrieval processes-and hampered exploration-executive control processes-in AD and potentially in MCI.

Detecting Apathy in Older Adults with Cognitive Disorders Using Automatic Speech Analysis.

BACKGROUND: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. OBJECTIVE: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy. METHODS: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. RESULTS: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females. CONCLUSIONS: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy.

Connectivity for healthcare and well-being management: examples from six European projects.

Technological advances and societal changes in recent years have contributed to a shift in traditional care models and in the relationship between patients and their doctors/carers, with (in general) an increase in the patient-carer physical distance and corresponding changes in the modes of access to relevant care information by all groups. The objective of this paper is to showcase the research efforts of six projects (that the authors are currently, or have recently been, involved in), CAALYX, eCAALYX, COGKNOW, EasyLine+, I2HOME, and SHARE-it, all funded by the European Commission towards a future where citizens can take an active role into managing their own healthcare. Most importantly, sensitive groups of citizens, such as the elderly, chronically ill and those suffering from various physical and cognitive disabilities, will be able to maintain vital and feature-rich connections with their families, friends and healthcare providers, who can then respond to, and prevent, the development of adverse health conditions in those they care for in a timely manner, wherever the carers and the people cared for happen to be.