Abstract
Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ...ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert’s diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading.
Parkinson's disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease's natural history and ...measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia-factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite's PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.
Parkinson's Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating ...severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.
In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smartphones, can ...be used to robustly extract objective and real-time measurements of human behavior. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this paper, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system. We propose a pipeline that initially processes each sensor signal separately, and then fuses both to perform the final detection. Features are extracted from each modality, and support vector machine (SVM) classifiers are used separately to perform snacking detection. Finally, we combine the SVM scores from both signals in a late-fusion scheme, which leads to increased eating detection accuracy. We evaluate the proposed eating monitoring system on a challenging, semifree living dataset of 14 subjects, which includes more than 60 h of audio and PPG signal recordings. Results show that fusing the audio and PPG signals significantly improves the effectiveness of eating event detection, achieving accuracy up to 0.938 and class-weighted accuracy up to 0.892.
•We propose a method for the indexing of point matches based on cross ratios.•Matches are characterised by the distribution of a scalar measure derived from multiple point relations.•Our consistency ...measure is processed statistically using random sampling.•We propose applications both in weekly calibrated and uncalibrated scenarios.•Our method exhibits low false positive rates without the use of thresholds.
We present a novel method that evaluates the geometric consistency of putative point matches in weakly calibrated settings, i.e. when the epipolar geometry but not the camera calibration is known, using only the point coordinates as information. The main idea behind our approach is the fact that each point correspondence in our data belongs to one of two classes (inliers/outlier). The classification of each point match relies on the histogram of a quantity representing the difference between cross ratios derived from a construction involving 6-tuples of point matches. Neither constraints nor scenario dependent parameters/thresholds are needed. Even for few candidate point matches the ensemble of 6-tuples containing each of them turns to provide statistically reliable histograms that prove to discriminate between inliers and outliers. In fact, in most cases a random sampling among this population is sufficient. Nevertheless, the accuracy of the method is positively correlated to its sampling density leading to an accuracy versus resulting computational complexity trade-off. Theoretical analysis and experiments are given that show the consistent performance of the proposed classification method when applied in inlier/outlier discrimination. The achieved accuracy is favourably evaluated against established methods that employ geometric only information, i.e. those relying on the Sampson, the algebraic and the symmetric epipolar distances. Finally, we also present an application of our scheme in uncalibrated stereo inside a RANSAC framework and compare it to the same as above methods.
Obesity in childhood and adolescence represents a major health problem. Novel e-Health technologies have been developed in order to provide a comprehensive and personalized plan of action for the ...prevention and management of overweight and obesity in childhood and adolescence. We used information and communication technologies to develop a "National Registry for the Prevention and Management of Overweight and Obesity" in order to register online children and adolescents nationwide, and to guide pediatricians and general practitioners regarding the management of overweight or obese subjects. Furthermore, intelligent multi-level information systems and specialized artificial intelligence algorithms are being developed with a view to offering precision and personalized medical management to obese or overweight subjects. Moreover, the Big Data against Childhood Obesity platform records behavioral data objectively by using inertial sensors and Global Positioning System (GPS) and combines them with data of the environment, in order to assess the full contextual framework that is associated with increased body mass index (BMI). Finally, a computerized decision-support tool was developed to assist pediatric health care professionals in delivering personalized nutrition and lifestyle optimization advice to overweight or obese children and their families. These e-Health applications are expected to play an important role in the management of overweight and obesity in childhood and adolescence.
(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or ...is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0 . 76 and a correlation coefficient of 0 . 874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0 . 089 and mean absolute error of 1 . 87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.
Unintentional weight loss has been observed among Parkinson's disease (PD) patients. Changes in energy intake (EI) and eating behavior, potentially caused by fine motor dysfunction and eating-related ...symptoms, might contribute to this. The primary aim of this study was to investigate differences in objectively measured EI between groups of healthy controls (HC), early (ESPD) and advanced stage PD patients (ASPD) during a standardized lunch in a clinical setting. The secondary aim was to identify clinical features and eating behavior abnormalities that explain EI differences. All participants (
= 23 HC,
= 20 ESPD, and
= 21 ASPD) went through clinical evaluations and were eating a standardized meal (200 g sausages, 400 g potato salad, 200 g apple purée and 500 mL water) in front of two video cameras. Participants ate freely, and the food was weighed pre- and post-meal to calculate EI (kcal). Multiple linear regression was used to explain group differences in EI. ASPD had a significantly lower EI vs. HC (-162 kcal,
< 0.05) and vs. ESPD (-203 kcal,
< 0.01) when controlling for sex. The number of spoonfuls, eating problems, dysphagia and upper extremity tremor could explain most (86%) of the lower EI vs. HC, while the first three could explain ~50% vs. ESPD. Food component intake analysis revealed significantly lower potato salad and sausage intakes among ASPD vs. both HC and ESPD, while water intake was lower vs. HC. EI is an important clinical target for PD patients with an increased risk of weight loss. Our results suggest that interventions targeting upper extremity tremor, spoonfuls, dysphagia and eating problems might be clinically useful in the prevention of unintentional weight loss in PD. Since EI was lower in ASPD, EI might be a useful marker of disease progression in PD.
Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy ...grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every day life. However, obtaining fine-grained ground-truth and remaining unobtrusive is a contradiction and therefore, the problem is usually addressed via multiple-instance learning. Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required. In contrast, large scale collection of data without any ground-truth is much easier. Nevertheless, utilizing unlabelled data in a multiple-instance setting is not straightforward, as the topic has received very little research attention. Here we try to fill this gap by introducing a new method for combining semi-supervised with multiple-instance learning. Our approach builds on the Virtual Adversarial Training principle, a state-of-the-art approach for regular semi-supervised learning, which we adapt and modify appropriately for the multiple-instance setting. We first establish the validity of the proposed approach through proof-of-concept experiments on synthetic problems generated from two well-known benchmark datasets. We then move on to the actual task of detecting PD tremor from hand acceleration signals collected in-the-wild, but in the presence of additional completely unlabelled data. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains (up to 9% increase in F1-score) in per-subject tremor detection for a cohort of 45 subjects with known tremor ground-truth. In doing so, we confirm the validity of our approach on a real-world problem where the need for semi-supervised and multiple-instance learning arises naturally.
Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their ...peers, despite food and environmental manipulations. Single meal measures may enable identification of "large portion eaters" and "fast eaters," finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated ("Less," "Average" or "More than peers"), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (
= 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (
= 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (
= 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (
= 0.74). The participants' recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (κ = 0.75 and κ = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (κ = 0.09 and κ = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings.
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