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Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some'eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari AH. Automated diagnosis of autism with artificial intelligence: State of the art. Rev Neurosci 2024; 35:141-163. [PMID: 37678819 DOI: 10.1515/revneuro-2023-0050] [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: 04/26/2023] [Accepted: 07/28/2023] [Indexed: 09/09/2023]
Abstract
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.
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Affiliation(s)
- Amir Valizadeh
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Mana Moassefi
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Amin Nakhostin-Ansari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Soheil Heidari Some'eh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Hossein Hosseini-Asl
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | | | - Reyhaneh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Zahra Maleki Ghorbani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Iman Menbari-Oskouie
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Faezeh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Research Development Center, Arash Women's Hospital, Tehran University of Medical Sciences, PO: 14695542, Tehran, Iran
| | - Alireza Mirzamohamadi
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Mohammad Ghafouri
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Shahriar Faghani
- Shariati Hospital, Department of Radiology, Tehran University of Medical Sciences, PO: 1411713135, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, PO: 1416634793, Tehran, Iran
| | - Amir Hossein Memari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
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Rządeczka M, Wodziński M, Moskalewicz M. Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity. Front Psychiatry 2023; 14:1291854. [PMID: 38116384 PMCID: PMC10729319 DOI: 10.3389/fpsyt.2023.1291854] [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: 09/10/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.
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Affiliation(s)
- Marcin Rządeczka
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
| | | | - Marcin Moskalewicz
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
- Philosophy of Mental Health Unit, Department of Social Sciences and the Humanities, Poznan University of Medical Sciences, Poznań, Poland
- Phenomenological Psychopathology and Psychotherapy, Psychiatric Clinic, University of Heidelberg, Heidelberg, Germany
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Wodziński M, Moskalewicz M. Mental Health Experts as Objects of Epistemic Injustice-The Case of Autism Spectrum Condition. Diagnostics (Basel) 2023; 13:927. [PMID: 36900070 PMCID: PMC10000601 DOI: 10.3390/diagnostics13050927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023] Open
Abstract
This theoretical paper addresses the issue of epistemic injustice with particular reference to autism. Injustice is epistemic when harm is performed without adequate reason and is caused by or related to access to knowledge production and processing, e.g., concerning racial or ethnic minorities or patients. The paper argues that both mental health service users and providers can be subject to epistemic injustice. Cognitive diagnostic errors often appear when complex decisions are made in a limited timeframe. In those situations, the socially dominant ways of thinking about mental disorders and half-automated and operationalized diagnostic paradigms imprint on experts' decision-making processes. Recently, analyses have focused on how power operates in the service user-provider relationship. It was observed that cognitive injustice inflicts on patients through the lack of consideration of their first-person perspectives, denial of epistemic authority, and even epistemic subject status, among others. This paper shifts focus toward health professionals as rarely considered objects of epistemic injustice. Epistemic injustice affects mental health providers by harming their access to and use of knowledge in their professional activities, thus affecting the reliability of their diagnostic assessments.
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Affiliation(s)
- Maciej Wodziński
- Institute of Philosophy, Maria Curie-Skłodowska University, M. Curie-Skłodowska sq. 4, 20-031 Lublin, Poland
- Doctoral School of Humanities, Maria Curie-Skłodowska University, Weteranów 18, 20-038 Lublin, Poland
| | - Marcin Moskalewicz
- Institute of Philosophy, Maria Curie-Skłodowska University, M. Curie-Skłodowska sq. 4, 20-031 Lublin, Poland
- Philosophy of Mental Health Unit, Department of Social Sciences and the Humanities, Poznan University of Medical Science, Rokietnicka 7, 60-806 Poznan, Poland
- Phenomenological Psychopathology and Psychotherapy, Psychiatric Clinic, Heidelberg University, Voßstraße 4, 69115 Heidelberg, Germany
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