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Lee SH, Paik SH, Kang SY, Phillips Z, Kim JB, Kim BJ, Kim BM. Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data. JOURNAL OF BIOPHOTONICS 2024:e202400138. [PMID: 38952169 DOI: 10.1002/jbio.202400138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/10/2024] [Accepted: 06/10/2024] [Indexed: 07/03/2024]
Abstract
Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.
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Affiliation(s)
- Seung Hyun Lee
- Global Health Technology Research Center, Korea University, Seoul, Republic of Korea
| | | | - Shin-Young Kang
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Zephaniah Phillips
- Global Health Technology Research Center, Korea University, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Byung-Jo Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Beop-Min Kim
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
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Scarpazza C, Costa C, Battaglia U, Berryessa C, Bianchetti ML, Caggiu I, Devinsky O, Ferracuti S, Focquaert F, Forgione A, Gilbert F, Pennati A, Pietrini P, Rainero I, Sartori G, Swerdlow R, Camperio Ciani AS. Acquired Pedophilia: international Delphi-method-based consensus guidelines. Transl Psychiatry 2023; 13:11. [PMID: 36653356 PMCID: PMC9849353 DOI: 10.1038/s41398-023-02314-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 12/22/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023] Open
Abstract
Idiopathic and acquired pedophilia are two different disorders with two different etiologies. However, the differential diagnosis is still very difficult, as the behavioral indicators used to discriminate the two forms of pedophilia are underexplored, and clinicians are still devoid of clear guidelines describing the clinical and neuroscientific investigations suggested to help them with this difficult task. Furthermore, the consequences of misdiagnosis are not known, and a consensus regarding the legal consequences for the two kinds of offenders is still lacking. The present study used the Delphi method to reach a global consensus on the following six topics: behavioral indicators/red flags helpful for differential diagnosis; neurological conditions potentially leading to acquired pedophilia; neuroscientific investigations important for a correct understanding of the case; consequences of misdiagnosis; legal consequences; and issues and future perspectives. An international and multidisciplinary board of scientists and clinicians took part in the consensus statements as Delphi members. The Delphi panel comprised 52 raters with interdisciplinary competencies, including neurologists, psychiatrists, neuropsychologists, forensic psychologists, expert in ethics, etc. The final recommendations consisted of 63 statements covering the six different topics. The current study is the first expert consensus on a delicate topic such as pedophilia. Important exploitable consensual recommendations that can ultimately be of immediate use by clinicians to help with differential diagnosis and plan and guide therapeutic interventions are described, as well as future perspectives for researchers.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Padova, Italy. .,Padova Neuroscience Center (PNC), University of Padova, Padova, Italy. .,IRCCS S. Camillo Hospital, Venezia, Italy.
| | - Cristiano Costa
- grid.5608.b0000 0004 1757 3470Padova Neuroscience Center (PNC), University of Padova, Padova, Italy ,grid.5608.b0000 0004 1757 3470Department of Neuroscience, University of Padova, Padova, Italy
| | - Umberto Battaglia
- grid.5608.b0000 0004 1757 3470Department of Applied Psychology, FISPPA – University of Padova, Padova, Italy
| | - Colleen Berryessa
- grid.430387.b0000 0004 1936 8796School of Criminal Justice, Rutgers University, Newark, NJ USA
| | - Maria Lucia Bianchetti
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padova, Padova, Italy
| | - Ilenia Caggiu
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padova, Padova, Italy
| | - Orrin Devinsky
- grid.137628.90000 0004 1936 8753Epilepsy Center, NYU School of Medicine, New York, USA
| | - Stefano Ferracuti
- grid.7841.aDepartment of Human Neurosciences Sapienza Università di Roma, Rome, Italy
| | - Farah Focquaert
- grid.5342.00000 0001 2069 7798Bioethics Institute Ghent, Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
| | - Arianna Forgione
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padova, Padova, Italy
| | - Fredric Gilbert
- grid.1009.80000 0004 1936 826XEthics, Policy & Public Engagement (EPPE) ARC Centre of Excellence for Electromaterials Science (ACES), Faculty of Arts, University of Tasmania, Hobart, Australia
| | | | - Pietro Pietrini
- grid.462365.00000 0004 1790 9464IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Innocenzo Rainero
- grid.7605.40000 0001 2336 6580Neurology I, Department of Neuroscience “Rita Levi Montalcini”, University of Torino, Turin, Italy
| | - Giuseppe Sartori
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padova, Padova, Italy
| | - Russell Swerdlow
- grid.412016.00000 0001 2177 6375University of Kansas Medical Center, Kansas City, KS USA
| | - Andrea S. Camperio Ciani
- grid.5608.b0000 0004 1757 3470Department of Applied Psychology, FISPPA – University of Padova, Padova, Italy
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Arooj S, Atta-ur-Rahman, Zubair M, Khan MF, Alissa K, Khan MA, Mosavi A. Breast Cancer Detection and Classification Empowered With Transfer Learning. Front Public Health 2022; 10:924432. [PMID: 35859776 PMCID: PMC9289190 DOI: 10.3389/fpubh.2022.924432] [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: 04/20/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022] Open
Abstract
Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.
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Latif S, XianWen F, Wang LL. Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique. JOURNAL OF INTELLIGENT SYSTEMS 2021. [DOI: 10.1515/jisys-2020-0065] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared
(
χ
)
\left(\chi )
test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.
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Affiliation(s)
- Sohaib Latif
- School of Mathematics and Big Data, Department of Computer Science, Anhui University of Science and Technology , Huainan , Anhui 232001 , China
| | - Fang XianWen
- School of Mathematics and Big Data, Department of Computer Science, Anhui University of Science and Technology , Huainan , Anhui 232001 , China
| | - Li-li Wang
- The Key Laboratory of Embedded System and Service Computing, Ministry of Education (Tongji University) , Shanghai 201804 , China
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Mechelli A, Vieira S. From models to tools: clinical translation of machine learning studies in psychosis. NPJ SCHIZOPHRENIA 2020; 6:4. [PMID: 32060287 PMCID: PMC7021680 DOI: 10.1038/s41537-020-0094-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 01/24/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Itani S, Rossignol M. At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability. Front Psychiatry 2020; 11:552262. [PMID: 33192664 PMCID: PMC7541948 DOI: 10.3389/fpsyt.2020.552262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 09/07/2020] [Indexed: 11/27/2022] Open
Affiliation(s)
- Sarah Itani
- Fund for Scientific Research (F.R.S.-FNRS), Brussels, Belgium.,Department of Mathematics and Operations Research, Faculty of Engineering, University of Mons, Mons, Belgium
| | - Mandy Rossignol
- Department of Cognitive Psychology and Neuropsychology, Faculty of Psychology and Education, University of Mons, Mons, Belgium
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