Christian Hook

A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements

Parkinsons disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individuals handwritten trace called Mean Relative Tremor is also presented.

A new computer vision-based approach to aid the diagnosis of Parkinson's disease

BACKGROUND AND OBJECTIVE Even today, pointing out an exam that can diagnose a patient with Parkinsons disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. METHODS In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. RESULTS The results have shown that we can obtain very reasonable recognition rates (around ≈67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. CONCLUSIONS The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients.

Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics

Parkinsons Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individuals exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research.

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

BACKGROUND AND OBJECTIVE Parkinsons disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. METHODS In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individuals assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. RESULTS The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. CONCLUSIONS The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinsons disease identification, as well as it can outperform handcrafted features.

Parkinson's Disease Identification through Deep Optimum-Path Forest Clustering

Approximately 50,000 to 60,000 new cases of Parkinsons disease (PD) are diagnosed yearly. Despite being non-lethal, PD shortens life expectancy of the ones affected with such disease. As such, researchers from different fields of study have put great effort in order to develop methods aiming the identification of PD in its early stages. This work uses handwriting dynamics data acquired by a series of tasks and proposes the application of a deep-driven graph-based clustering algorithm known as Optimum-Path Forest to learn a dictionary-like representation of each individual in order to automatic identify Parkinsons disease. Experimental results have shown promising results, with results comparable to some state-of-the-art approaches in the literature.

A survey on Barrett's esophagus analysis using machine learning

This work presents a systematic review concerning recent studies and technologies of machine learning for Barretts esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.

A survey on computer-assisted Parkinson's Disease diagnosis

BACKGROUND AND OBJECTIVE In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinsons Disease (PD), as well as an analysis of future trends on new approaches to this end. METHODS In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. RESULTS The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and e-health monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis. CONCLUSIONS The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016.

Parkinson’s Disease Identification Using Restricted Boltzmann Machines

Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60, 000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes.

Barrett’s Esophagus Analysis Using SURF Features

The development of adenocarcinoma in Barrett’s esophagus is difficult to detect by endoscopic surveillance of patients with signs of dysplasia. Computer assisted diagnosis of endoscopic images (CAD) could therefore be most helpful in the demarcation and classification of neoplastic lesions. In this study we tested the feasibility of a CAD method based on Speeded up Robust Feature Detection (SURF). A given database containing 100 images from 39 patients served as benchmark for feature based classification models. Half of the images had previously been diagnosed by five clinical experts as being ”cancerous”, the other half as ”non-cancerous”. Cancerous image regions had been visibly delineated (masked) by the clinicians. SURF features acquired from full images as well as from masked areas were utilized for the supervised training and testing of an SVM classifier. The predictive accuracy of the developed CAD system is illustrated by sensitivity and specificity values. The results based on full image matching where 0.78 (sensitivity) and 0.82 (specificity) were achieved, while the masked region approach generated results of 0.90 and 0.95, respectively.