1
|
Paveliev M, Egorchev AA, Musin F, Lipachev N, Melnikova A, Gimadutdinov RM, Kashipov AR, Molotkov D, Chickrin DE, Aganov AV. Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence. Int J Mol Sci 2024; 25:4227. [PMID: 38673819 PMCID: PMC11049984 DOI: 10.3390/ijms25084227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/31/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024] Open
Abstract
Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer's disease, stroke, post-traumatic conditions, and some other brain disorders. The functional role of the PNN microstructure in brain pathologies has remained largely unstudied until recently. Here, we review recent research implicating PNN microstructural changes in schizophrenia and other disorders. We further concentrate on high-resolution studies of the PNN mesh units surrounding synaptic boutons to elucidate fine structural details behind the mutual functional regulation between the ECM and the synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain's complexity through a wide range of organization levels-from synaptic molecular events to large scale tissue rearrangements and the whole-brain connectome function. This scope matches exactly the complex role of PNN in brain physiology and pathology processes, and the first AI-assisted PNN microscopy studies have been reported. To that end, we report here on a machine learning-assisted tool for PNN mesh contour tracing.
Collapse
Affiliation(s)
- Mikhail Paveliev
- Neuroscience Center, University of Helsinki, Haartmaninkatu 8, 00290 Helsinki, Finland
| | - Anton A. Egorchev
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia; (A.A.E.); (F.M.); (R.M.G.)
| | - Foat Musin
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia; (A.A.E.); (F.M.); (R.M.G.)
| | - Nikita Lipachev
- Institute of Physics, Kazan Federal University, Kremlyovskaya 16a, Kazan 420008, Tatarstan, Russia; (N.L.); (A.V.A.)
| | - Anastasiia Melnikova
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Karl Marx 74, Kazan 420015, Tatarstan, Russia;
| | - Rustem M. Gimadutdinov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, Kremlyovskaya 35, Kazan 420008, Tatarstan, Russia; (A.A.E.); (F.M.); (R.M.G.)
| | - Aidar R. Kashipov
- Institute of Artificial Intelligence, Robotics and Systems Engineering, Kazan Federal University, Kremlyovskaya 18, Kazan 420008, Tatarstan, Russia; (A.R.K.); (D.E.C.)
| | - Dmitry Molotkov
- Biomedicum Imaging Unit, University of Helsinki, Haartmaninkatu 8, 00014 Helsinki, Finland;
| | - Dmitry E. Chickrin
- Institute of Artificial Intelligence, Robotics and Systems Engineering, Kazan Federal University, Kremlyovskaya 18, Kazan 420008, Tatarstan, Russia; (A.R.K.); (D.E.C.)
| | - Albert V. Aganov
- Institute of Physics, Kazan Federal University, Kremlyovskaya 16a, Kazan 420008, Tatarstan, Russia; (N.L.); (A.V.A.)
| |
Collapse
|
2
|
Praetorius JP, Walluks K, Svensson CM, Arnold D, Figge MT. IMFSegNet: Cost-effective and objective quantification of intramuscular fat in histological sections by deep learning. Comput Struct Biotechnol J 2023; 21:3696-3704. [PMID: 37560127 PMCID: PMC10407270 DOI: 10.1016/j.csbj.2023.07.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/11/2023] Open
Abstract
The assessment of muscle condition is of great importance in various research areas. In particular, evaluating the degree of intramuscular fat (IMF) in tissue sections is a challenging task, which today is still mostly performed qualitatively or quantitatively by a highly subjective and error-prone manual analysis. We here realize the mission to make automated IMF analysis possible that (i) minimizes subjectivity, (ii) provides accurate and quantitative results quickly, and (iii) is cost-effective using standard hematoxylin and eosin (H&E) stained tissue sections. To address all these needs in a deep learning approach, we utilized the convolutional encoder-decoder network SegNet to train the specialized network IMFSegNet allowing to accurately quantify the spatial distribution of IMF in histological sections. Our fully automated analysis was validated on 17 H&E-stained muscle sections from individual sheep and compared to various state-of-the-art approaches. Not only does IMFSegNet outperform all other approaches, but this neural network also provides fully automated and highly accurate results utilizing the most cost-effective procedures of sample preparation and imaging. Furthermore, we shed light on the opacity of black-box approaches such as neural networks by applying an explainable artificial intelligence technique to clarify that the success of IMFSegNet actually lies in identifying the hard-to-detect IMF structures. Embedded in our open-source visual programming language JIPipe that does not require programming skills, it can be expected that IMFSegNet advances muscle condition assessment in basic research across multiple areas as well as in research fields focusing on translational clinical applications.
Collapse
Affiliation(s)
- Jan-Philipp Praetorius
- Applied Systems Biology, Leibniz institute for natural Product Research and infection Biology – Hans Knöll institute (HKI), Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany
| | - Kassandra Walluks
- Applied Systems Biology, Leibniz institute for natural Product Research and infection Biology – Hans Knöll institute (HKI), Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany
- Institute of Zoology and Evolutionary Research, Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany
| | - Carl-Magnus Svensson
- Applied Systems Biology, Leibniz institute for natural Product Research and infection Biology – Hans Knöll institute (HKI), Jena, Germany
| | - Dirk Arnold
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
- Facial-Nerve-Center Jena, Jena University Hospital, Jena, Germany
| | - Marc Thilo Figge
- Applied Systems Biology, Leibniz institute for natural Product Research and infection Biology – Hans Knöll institute (HKI), Jena, Germany
- Institute of Microbiology, Faculty of Biological Sciences, Friedrich Schiller University Jena, Jena, Germany
| |
Collapse
|
3
|
Luo J, Hu M. A bipolar three-way decision model and its application in analyzing incomplete data. Int J Approx Reason 2023. [DOI: 10.1016/j.ijar.2022.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
4
|
Zhang F, Zheng Y, Wu J, Yang X, Che X. Multi-rater label fusion based on an information bottleneck for fundus image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
5
|
Esteves LG, Izbicki R, Stern JM, Stern RB. Logical coherence in Bayesian simultaneous three-way hypothesis tests. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
6
|
Ye J, Sun B, Zhan J, Chu X. Variable precision multi-granulation composite rough sets with multi-decision and their applications to medical diagnosis. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
7
|
Kang Y, Yu B, Cai M. Multi-attribute predictive analysis based on attribute-oriented fuzzy rough sets in fuzzy information systems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
8
|
Liu X, Mao W, Dai J, Zhang K. Comprehensive fuzzy concept-oriented three-way decision and its application. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
9
|
Learning to count biological structures with raters’ uncertainty. Med Image Anal 2022; 80:102500. [DOI: 10.1016/j.media.2022.102500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/22/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
|
10
|
Hildebrandt M. The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems. Front Artif Intell 2022; 5:789076. [PMID: 35573902 PMCID: PMC9096719 DOI: 10.3389/frai.2022.789076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU's General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act).
Collapse
Affiliation(s)
- Mireille Hildebrandt
- Institute of Computing and Information Sciences (iCIS), Science Faculty, Radboud University, Nijmegen, Netherlands
- Research Group Law Science Technology & Society (LSTS), Faculty of Law and Criminology, Vrije Universiteit Brussel, Brussels, Belgium
- *Correspondence: Mireille Hildebrandt
| |
Collapse
|
11
|
On three perspectives for deriving three-way decision with linguistic intuitionistic fuzzy information. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.072] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
12
|
Zhang X, Chen J. Three-hierarchical three-way decision models for conflict analysis: A qualitative improvement and a quantitative extension. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
13
|
|
14
|
Three-way decision model under a large-scale group decision-making environment with detecting and managing non-cooperative behaviors in consensus reaching process. Artif Intell Rev 2022. [DOI: 10.1007/s10462-021-10133-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
15
|
Ye J, Zhan J, Ding W, Fujita H. A novel three-way decision approach in decision information systems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
16
|
Liu J, Li H, Huang B, Liu Y, Liu D. Convex combination-based consensus analysis for intuitionistic fuzzy three-way group decision. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
17
|
A Novel Multi-attribute Group Decision-Making Method Based on q-Rung Dual Hesitant Fuzzy Information and Extended Power Average Operators. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09932-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
18
|
Wu C, Zhang Q, Zhao F, Cheng Y, Wang G. Three-way recommendation model based on shadowed set with uncertainty invariance. Int J Approx Reason 2021. [DOI: 10.1016/j.ijar.2021.04.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
19
|
Ronzio L, Campagner A, Cabitza F, Gensini GF. Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology. J Intell 2021; 9:jintelligence9020017. [PMID: 33915991 PMCID: PMC8167709 DOI: 10.3390/jintelligence9020017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/21/2021] [Accepted: 03/09/2021] [Indexed: 12/03/2022] Open
Abstract
Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing interest as a means of reducing error rates and their impacts. Previous studies have shown that a combination of individual opinions based on rules, weighting mechanisms, or other CI solutions could improve diagnostic accuracy with respect to individual doctors. We conducted a study to investigate the potential of this approach in cardiology and, more precisely, in electrocardiogram (ECG) reading. To achieve this aim, we designed and conducted an experiment involving medical students, recent graduates, and residents, who were asked to annotate a collection of 10 ECGs of various complexity and difficulty. For each ECG, we considered groups of increasing size (from three to 30 members) and applied three different CI protocols. In all cases, the results showed a statistically significant improvement (ranging from 9% to 88%) in terms of diagnostic accuracy when compared to the performance of individual readers; this difference held for not only large groups, but also smaller ones. In light of these results, we conclude that CI approaches can support the tasks mentioned above, and possibly other similar ones as well. We discuss the implications of applying CI solutions to clinical settings, such as cases of augmented ‘second opinions’ and decision-making.
Collapse
Affiliation(s)
- Luca Ronzio
- Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; (L.R.); (A.C.)
| | - Andrea Campagner
- Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; (L.R.); (A.C.)
| | - Federico Cabitza
- Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; (L.R.); (A.C.)
- Correspondence:
| | | |
Collapse
|
20
|
Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review. ROUGH SETS 2020. [PMCID: PMC7338178 DOI: 10.1007/978-3-030-52705-1_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is used to identify and properly take into account the uncertain instances; and handling the uncertainty lying in the output, where TWD is used to allow the ML model to abstain. We then present a narrative review of the state of the art of applications of TWD in regard to the different areas of concern identified by the framework, and in so doing, we will highlight both the points of strength of the three-way methodology, and the opportunities for further research.
Collapse
|