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Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test
The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool,...
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| Published in: | NPJ Parkinson's Disease 2022-04, Vol.8 (1), p.42-9, Article 42 |
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| creator | Ortelli, Paola Ferrazzoli, Davide Versace, Viviana Cian, Veronica Zarucchi, Marianna Gusmeroli, Anna Canesi, Margherita Frazzitta, Giuseppe Volpe, Daniele Ricciardi, Lucia Nardone, Raffaele Ruffini, Ingrid Saltuari, Leopold Sebastianelli, Luca Baranzini, Daniele Maestri, Roberto |
| description | The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (
p
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| doi_str_mv | 10.1038/s41531-022-00304-z |
| format | article |
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p
< 0.0001,
p
= 0.028 and
p
= 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.
This study has been registered on ClinicalTrials.gov (NCT04858893).</description><identifier>ISSN: 2373-8057</identifier><identifier>EISSN: 2373-8057</identifier><identifier>DOI: 10.1038/s41531-022-00304-z</identifier><identifier>PMID: 35410449</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/617/375/346/1718 ; 692/700/139/422 ; Artificial intelligence ; Biomedical and Life Sciences ; Biomedicine ; Brain diseases ; Cognition & reasoning ; Discriminant analysis ; Neurology ; Neuropsychology ; Neurosciences ; Parkinson's disease</subject><ispartof>NPJ Parkinson's Disease, 2022-04, Vol.8 (1), p.42-9, Article 42</ispartof><rights>The Author(s) 2022</rights><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-1db7a435ece53a1d44e679e00448dbdd9372255007f431b55ead09941fa1043</citedby><cites>FETCH-LOGICAL-c540t-1db7a435ece53a1d44e679e00448dbdd9372255007f431b55ead09941fa1043</cites><orcidid>0000-0002-2873-0430</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2649216930/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2649216930?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35410449$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ortelli, Paola</creatorcontrib><creatorcontrib>Ferrazzoli, Davide</creatorcontrib><creatorcontrib>Versace, Viviana</creatorcontrib><creatorcontrib>Cian, Veronica</creatorcontrib><creatorcontrib>Zarucchi, Marianna</creatorcontrib><creatorcontrib>Gusmeroli, Anna</creatorcontrib><creatorcontrib>Canesi, Margherita</creatorcontrib><creatorcontrib>Frazzitta, Giuseppe</creatorcontrib><creatorcontrib>Volpe, Daniele</creatorcontrib><creatorcontrib>Ricciardi, Lucia</creatorcontrib><creatorcontrib>Nardone, Raffaele</creatorcontrib><creatorcontrib>Ruffini, Ingrid</creatorcontrib><creatorcontrib>Saltuari, Leopold</creatorcontrib><creatorcontrib>Sebastianelli, Luca</creatorcontrib><creatorcontrib>Baranzini, Daniele</creatorcontrib><creatorcontrib>Maestri, Roberto</creatorcontrib><title>Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test</title><title>NPJ Parkinson's Disease</title><addtitle>npj Parkinsons Dis</addtitle><addtitle>NPJ Parkinsons Dis</addtitle><description>The assessment of cognitive deficits is pivotal for diagnosis and management in patients with parkinsonisms. Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (
p
< 0.0001,
p
= 0.028 and
p
= 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.
This study has been registered on ClinicalTrials.gov (NCT04858893).</description><subject>692/617/375/346/1718</subject><subject>692/700/139/422</subject><subject>Artificial intelligence</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Brain diseases</subject><subject>Cognition & reasoning</subject><subject>Discriminant analysis</subject><subject>Neurology</subject><subject>Neuropsychology</subject><subject>Neurosciences</subject><subject>Parkinson's disease</subject><issn>2373-8057</issn><issn>2373-8057</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9ks1u1DAUhSMEolXpC7BAkdiwCVzHdhxvkFDFT6VKRYK95dg3qYfEDnam0gwvj2dSSsuClS373M8-R6coXhJ4S4C27xIjnJIK6roCoMCq_ZPitKaCVi1w8fTB_qQ4T2kDAIQ1reTwvDihnBFgTJ4Wv67nxU1urxcXfBn60oTBu8XdYqlTwpQm9EvpfPlVxx_Op-BdmlLZ7Uo9z-PO-aHUcXG9M06PWbfgOLoBvcFyCaXOuGmOeIM-HZDJRER_GFowLS-KZ70eE57frWfFt08fv198qa6uP19efLiqDGewVMR2QjPK0SCnmljGsBESIRtobWetpKKuOQcQPaOk4xy1BSkZ6XU2Sc-Ky5Vqg96oObpJx50K2qnjQYiDOjgwIypLNBOiazvaSCaMaYWFlnZ9bSk0RurMer-y5m03oTU5m6jHR9DHN97dqCHcKpnTF5xmwJs7QAw_tzkDNblkcmbaY9gmVTdM8rZtWZ2lr_-RbsI2-hzUUVWTRlLIqnpVmRhSitjff4aAOhRFrUVRuSjqWBS1z0OvHtq4H_lTiyygqyDlKz9g_Pv2f7C_AY0_zC0</recordid><startdate>20220411</startdate><enddate>20220411</enddate><creator>Ortelli, Paola</creator><creator>Ferrazzoli, Davide</creator><creator>Versace, Viviana</creator><creator>Cian, Veronica</creator><creator>Zarucchi, Marianna</creator><creator>Gusmeroli, Anna</creator><creator>Canesi, Margherita</creator><creator>Frazzitta, Giuseppe</creator><creator>Volpe, Daniele</creator><creator>Ricciardi, Lucia</creator><creator>Nardone, Raffaele</creator><creator>Ruffini, Ingrid</creator><creator>Saltuari, Leopold</creator><creator>Sebastianelli, Luca</creator><creator>Baranzini, Daniele</creator><creator>Maestri, Roberto</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><general>Nature Portfolio</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2873-0430</orcidid></search><sort><creationdate>20220411</creationdate><title>Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test</title><author>Ortelli, Paola ; 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Low levels of correspondence are observed between evaluations assessed with screening cognitive tests in comparison with those assessed with in-depth neuropsychological batteries. A new tool, we named CoMDA (Cognition in Movement Disorders Assessment), was composed by merging Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Frontal Assessment Battery (FAB). In total, 500 patients (400 with Parkinson’s disease, 41 with vascular parkinsonism, 31 with progressive supranuclear palsy, and 28 with multiple system atrophy) underwent CoMDA (level 1–L1) and in-depth neuropsychological battery (level 2–L2). Machine learning was developed to classify the CoMDA score and obtain an accurate prediction of the cognitive profile along three different classes: normal cognition (NC), mild cognitive impairment (MCI), and impaired cognition (IC). The classification accuracy of CoMDA, assessed by ROC analysis, was compared with MMSE, MoCA, and FAB. The area under the curve (AUC) of CoMDA was significantly higher than that of MMSE, MoCA and FAB (
p
< 0.0001,
p
= 0.028 and
p
= 0.0007, respectively). Among 15 different algorithmic methods, the Quadratic Discriminant Analysis algorithm (CoMDA-ML) showed higher overall-metrics performance levels in predictive performance. Considering L2 as a 3-level continuous feature, CoMDA-ML produces accurate and generalizable classifications: micro-average ROC curve, AUC = 0.81; and AUC = 0.85 for NC, 0.67 for MCI, and 0.83 for IC. CoMDA and COMDA-ML are reliable and time-sparing tools, accurate in classifying cognitive profile in parkinsonisms.
This study has been registered on ClinicalTrials.gov (NCT04858893).</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>35410449</pmid><doi>10.1038/s41531-022-00304-z</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-2873-0430</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 692/617/375/346/1718 692/700/139/422 Artificial intelligence Biomedical and Life Sciences Biomedicine Brain diseases Cognition & reasoning Discriminant analysis Neurology Neuropsychology Neurosciences Parkinson's disease |
| title | Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test |
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