Automatic Cataract Detection Using the Convolutional Neural Network and Digital Camera Images

Background: The cataract is the most prevalent cause of blindness worldwide and is responsible for more than 51 % of blindness cases. As the treatment process is becoming smart and the burden of ophthalmologists is reducing, many existing systems have adopted machine-learning-based cataract classification methods with manual extraction of data features. However, the manual extraction of retinal features is generally time-consuming and exhausting and requires skilled ophthalmologists. Material and Methods: Convolutional neural network (CNN) is a highly common automatic feature extraction model which, compared to machine learning approaches, requires much larger datasets to avoid overfitting issues. This article designs a deep convolutional network for automatic cataract recognition in healthy eyes. The algorithm consists of four convolution layers and a fully connected layer for hierarchical feature learning and training. Results: The proposed approach was tested on collected images and indicated an 90.88 % accuracy on testing data. The keras model provides a function that evaluates the model, which is equal to the value of 84.14 %, the model can be further developed and improved to be applied for the automatic recognition and treatment of ocular diseases. Conclusion: This study presented a deep learning algorithm for the automatic recognition of healthy eyes from cataractous ones. The results suggest that the proposed scheme outperforms other conventional methods and can be regarded as a reference for other retinal disorders

Tiled Sparse Coding in Eigenspaces for Image Classification

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.MCIN/ AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 projectConsejería de890 Economía, Innovación, Ciencia y Empleo (Junta de Andalucía)FEDER under CV20-45250, A- TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 project

The effect of bright light on sleep in nursing home patients with dementia

Background: Up to 70% of nursing home patients with dementia suffer from disrupted sleep, often characterized by multiple awakenings at night and excessive daytime sleep. Sleep disruption may have negative effects on the cognition, mood, behaviour, and well-being of nursing home patients, while also representing a challenge for nursing home staff. However, few sleep scales are developed and validated specifically for the nursing home setting. Sleep problems among nursing home patients are frequently treated by medications, which are associated with severe side effects, including daytime sleepiness, and an increased risk of falls. Thus, there is a need for non-pharmacological interventions to improve sleep in this population. Bright light treatment (BLT) may represent such an intervention, providing increased light exposure aiming to impact sleep, circadian rhythmicity, mood, and/or behaviour. Light is the most important zeitgeber to the circadian system, and consequently has a significant impact on sleep-wake behaviour. Unfortunately, studies have reported low indoor light levels in nursing homes, which in combination with dementia-related neuropathology and age-related reductions in light sensitivity, are likely to contribute to sleep problems in this population. The aim of this thesis was to investigate whether increasing daytime light exposure, by means of BLT, can improve sleep in nursing home patients with dementia, and also to address methodological challenges in this field of research. Methods: Paper 1 is a systematic review of the literature, focusing on the methodological features of the included studies, in addition to their findings. Paper 2 and 3 are based on data from the DEM.LIGHT trial; a cluster-randomized placebo-controlled trial conducted in Norwegian nursing homes, including 69 patients. The intervention comprised a diurnal cycle of ambient light with a maximum of 1,000 lux and 6,000 Kelvin (K) from 10:00-15:00, administered using light emitting diode (LED) light. Before and after this interval, the light levels gradually increased/decreased in lux and K. In the placebo condition, standard light levels were maintained at 150-300 lux and approximately 3,000 K throughout the day. The intervention and placebo lights were installed in the common rooms of the included nursing home units. Outcomes were measured at baseline and at follow-up at week 8, 16, and 24. Paper 2 was a validation study of a proxy-rated sleep scale, using the baseline data from the DEM. LIGHT trial. Actigraphy was used as the reference standard. Paper 3 reported on the sleep outcomes of the trial, which were the primary outcomes. Results: Paper 1 found that there are promising, though inconsistent, results regarding the effect of BLT on sleep and circadian rhythmicity in dementia. Large heterogeneity in terms of interventions, study designs, population characteristics, and outcome measurement tools may explain some of the inconsistencies of results across studies. Paper 2 showed that the proxy-rated Sleep Disorder Inventory (SDI) had satisfactory internal consistency and convergent validity. Using actigraphy as the reference standard, the SDI was termed clinically useful, and we suggested a cut-off score of five or more as defining disrupted sleep in nursing home patients with dementia. These results should be interpreted keeping in mind that actigraphy have some important weaknesses, such as underestimating wake time. Paper 3 evaluated the effects of the BLT on sleep and found an improvement in sleep according to the SDI scores in the intervention group, as compared to the control group, from baseline to week 16 and baseline to week 24. There was no effect in terms of sleep measured by actigraphy. Conclusion: In summary, this thesis found that the evidence for an effect of BLT on sleep in nursing home patients with dementia is promising, but equivocal. Importantly, the research field faces some important methodological challenges, such as accurately measuring sleep. The SDI may represent a valid tool to measure sleep in the nursing home setting, which may be used both by researchers and by practitioners. Although the results of this thesis are not conclusive regarding the effect of BLT on sleep in nursing home patients with dementia, it may represent a step forward in understanding the potential value of BLT in this population, and may lay the ground for further investigation. The lack of an improvement on the SDI at week 8 indicates that the effect of BLT may take a long time to manifest in this population.Doktorgradsavhandlin

Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes

Abstracts of 51st EASD Annual Meeting

Background and aims: Presence and frequency of beta cell (BC) dysfunction(BCD) and insulin resistance (IR) in patients with newly diagnosedtype 2 diabetes mellitus (NDT2D) are imperfectly known, becauseprevious studies used small cohorts and/or only surrogate indexes of BCfunction and IR.We sought to assess BC function and IR with state-of-artmethods in the VNDS.Materials and methods: In 712 GADA-negative, drug naïve, consecutiveItalian NDT2D patients we assessed: 1. standard parameters; 2. insulinsensitivity (IS) by the euglycaemic insulin clamp); 3. BC functionby state-of-art modeling of prolonged (5 hours) OGTT-derived glucose/C-peptide curves. Thresholds for BCD and IR were the 25th percentilesof BC function and IS assessed with the same methods of the VNDS inItalian subjects with normal glucose regulation of the GENFIEV (n=340)and GISIR (n=386) studies, respectively.Results: In the VNDS, 89.8% [95% C.I.: 87.6 - 92.0%] and87.8% [85.4 - 90.2] patients had BCD and IR, respectively. Patientswith only one defect were 19.7% [16.8 - 22.6]. IsolatedBCD and isolated IR were present in 10.9% [8.6 - 13.2] and8.9% [6.8 - 11.0] patients, respectively. Coexistence of BCDand IR was observed in 78.9% [75.9 - 81.9] of the patients.1.4% [0.5 - 2.3] of the patients had no detectable alterations inBC function and IS. Patients (19.7%) with only one metabolicdefect had lower BMI, fasting glucose, HbA1c, triglycerides andBC function, and higher HDL-cholesterol and IS than patientswith both BCD and IR (p<0.01 or less after Bonferroni’scorrection).Conclusion: In conclusion, in NDT2DM patients: 1. at least 75.9% haveboth BCD and IR; 2. At least 87.6% and 85.4% have BCD and IR,respectively; 3. At least 16.8% have only one defect and a significantlydifferent (milder) metabolic phenotype compared to patients with bothdefects. These findings may be relevant to therapeutic strategies centeredon the metabolic phenotype of the patient.Clinical Trial Registration Number: NCT00879801; NCT01526720Supported by: University of Veron