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Dimauro G, Griseta ME, Camporeale MG, Clemente F, Guarini A, Maglietta R. An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artif Intell Med 2023; 136:102477. [PMID: 36710064 DOI: 10.1016/j.artmed.2022.102477] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022]
Abstract
Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities.
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Affiliation(s)
- Giovanni Dimauro
- Department of Computer Science, University of Bari 'Aldo Moro', Bari, Italy.
| | - Maria Elena Griseta
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy.
| | | | - Felice Clemente
- Haematology Dept. of National Cancer Institute 'Giovanni Paolo II', Bari, Italy.
| | - Attilio Guarini
- Haematology Dept. of National Cancer Institute 'Giovanni Paolo II', Bari, Italy.
| | - Rosalia Maglietta
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy.
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Jiang Y, Song L, Zhang J, Song Y, Yan M. Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:5855. [PMID: 35957417 PMCID: PMC9371015 DOI: 10.3390/s22155855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 05/14/2023]
Abstract
Gesture recognition based on wearable devices is one of the vital components of human-computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models' test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.
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Affiliation(s)
- Yujian Jiang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Communication University of China, Beijing 100024, China
- Beijing Key Laboratory of Modern Entertainment Technology, Communication University of China, Beijing 100024, China
- School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Lin Song
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Communication University of China, Beijing 100024, China
- Beijing Key Laboratory of Modern Entertainment Technology, Communication University of China, Beijing 100024, China
- School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Junming Zhang
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Communication University of China, Beijing 100024, China
- Beijing Key Laboratory of Modern Entertainment Technology, Communication University of China, Beijing 100024, China
- School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Yang Song
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Communication University of China, Beijing 100024, China
- Beijing Key Laboratory of Modern Entertainment Technology, Communication University of China, Beijing 100024, China
- School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Ming Yan
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Communication University of China, Beijing 100024, China
- Beijing Key Laboratory of Modern Entertainment Technology, Communication University of China, Beijing 100024, China
- School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
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Galaz Z, Drotar P, Mekyska J, Gazda M, Mucha J, Zvoncak V, Smekal Z, Faundez-Zanuy M, Castrillon R, Orozco-Arroyave JR, Rapcsak S, Kincses T, Brabenec L, Rektorova I. Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset. Front Neuroinform 2022; 16:877139. [PMID: 35722168 PMCID: PMC9198652 DOI: 10.3389/fninf.2022.877139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN).
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Affiliation(s)
- Zoltan Galaz
- Department of Telecommunications, Brno University of Technology, Brno, Czechia
| | - Peter Drotar
- Intelligent Information Systems Laboratory, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Košice, Slovakia
| | - Jiri Mekyska
- Department of Telecommunications, Brno University of Technology, Brno, Czechia
| | - Matej Gazda
- Intelligent Information Systems Laboratory, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Košice, Slovakia
| | - Jan Mucha
- Department of Telecommunications, Brno University of Technology, Brno, Czechia
| | - Vojtech Zvoncak
- Department of Telecommunications, Brno University of Technology, Brno, Czechia
| | - Zdenek Smekal
- Department of Telecommunications, Brno University of Technology, Brno, Czechia
| | | | - Reinel Castrillon
- Faculty of Engineering, Universidad de Antioquia—UdeA, Medellín, Colombia
- Faculty of Engineering, Universidad Católica de Oriente, Rionegro, Colombia
| | - Juan Rafael Orozco-Arroyave
- Faculty of Engineering, Universidad de Antioquia—UdeA, Medellín, Colombia
- Pattern Recognition Lab, Friedrich-Alexander-Universität, Erlangen, Germany
| | - Steven Rapcsak
- Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, United States
| | - Tamas Kincses
- Department of Neurology, University of Szeged, Szeged, Hungary
| | - Lubos Brabenec
- Applied Neuroscience Research Group, Central European Institute of Technology—CEITEC, Masaryk University, Brno, Czechia
| | - Irena Rektorova
- Applied Neuroscience Research Group, Central European Institute of Technology—CEITEC, Masaryk University, Brno, Czechia
- First Department of Neurology, Faculty of Medicine and St. Anne's University Hospital, Masaryk University, Brno, Czechia
- *Correspondence: Irena Rektorova
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Cilia ND, D'Alessandro T, De Stefano C, Fontanella F, Molinara M. From online handwriting to synthetic images for Alzheimer's disease detection using a deep transfer learning approach. IEEE J Biomed Health Inform 2021; 25:4243-4254. [PMID: 34347614 DOI: 10.1109/jbhi.2021.3101982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of such diseases. For this reason, the analysis of handwriting and the study of its alterations have become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. In order to verify the importance and the specific role played by shape information, we also generated an off-line synthetic binary image for each handwriting sample, where background pixels have white color, while those corresponding to the traits have black color. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. We carried out a large set of experiments for comparing the results obtained by using on-line features with those obtained by using the off-line features provided by CNN on both color and binary images.
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Amboni M, Ricciardi C, Picillo M, De Santis C, Ricciardelli G, Abate F, Tepedino MF, D'Addio G, Cesarelli G, Volpe G, Calabrese MC, Cesarelli M, Barone P. Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages. Sci Rep 2021; 11:9297. [PMID: 33927317 PMCID: PMC8084977 DOI: 10.1038/s41598-021-88877-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/16/2021] [Indexed: 12/22/2022] Open
Abstract
Progressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait evaluation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.
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Affiliation(s)
- Marianna Amboni
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy. .,IDC Hermitage-Capodimonte, Naples, Italy.
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Marina Picillo
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Chiara De Santis
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Gianluca Ricciardelli
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Filomena Abate
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Maria Francesca Tepedino
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | | | - Giuseppe Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Naples, Italy
| | - Giampiero Volpe
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Maria Consiglia Calabrese
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Mario Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Paolo Barone
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
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Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine. J Pers Med 2021; 11:jpm11010032. [PMID: 33430240 PMCID: PMC7825660 DOI: 10.3390/jpm11010032] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/31/2020] [Accepted: 01/05/2021] [Indexed: 02/06/2023] Open
Abstract
With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.
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Affiliation(s)
- Oliwia Koteluk
- Faculty of Medical Sciences, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (O.K.); (A.W.)
| | - Adrian Wartecki
- Faculty of Medical Sciences, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (O.K.); (A.W.)
| | - Sylwia Mazurek
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland;
- Department of Cancer Diagnostics and Immunology, Greater Poland Cancer Centre, 61-866 Poznan, Poland
- Correspondence: ; Tel.: +48-61-885-06-67
| | - Iga Kołodziejczak
- Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02-091 Warsaw, Poland;
| | - Andrzej Mackiewicz
- Department of Cancer Immunology, Chair of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznan, Poland;
- Department of Cancer Diagnostics and Immunology, Greater Poland Cancer Centre, 61-866 Poznan, Poland
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Levodopa improves handwriting and instrumental tasks in previously treated patients with Parkinson's disease. J Neural Transm (Vienna) 2020; 127:1369-1376. [PMID: 32813086 PMCID: PMC7497291 DOI: 10.1007/s00702-020-02246-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/13/2020] [Indexed: 01/27/2023]
Abstract
Motor symptoms in patients with Parkinson's disease may be determined with instrumental tests and rating procedures. Their outcomes reflect the functioning and the impairment of the individual patient when patients are tested off and on dopamine substituting drugs. Objectives were to investigate whether the execution speed of a handwriting task, instrumentally assessed fine motor behavior, and rating scores improve after soluble levodopa application. 38 right-handed patients were taken off their regular drug therapy for at least 12 h before scoring, handwriting, and performance of instrumental devices before and 1 h after 100 mg levodopa intake. The outcomes of all performed procedures improved. The easy-to-perform handwriting task and the instrumental tests demand for fast and precise execution of movement sequences with considerable cognitive load in the domains' attention and concentration. These investigations may serve as additional tools for the testing of the dopaminergic response.
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