Il contributo dell’intelligenza artificiale nella diagnosi dei disturbi neurodegenerativi

Titolo Rivista RIVISTA SPERIMENTALE DI FRENIATRIA
Autori/Curatori Raffaele Nappo, Roberta Simeoli
Anno di pubblicazione 2024 Fascicolo 2024/3
Lingua Italiano Numero pagine 14 P. 131-144 Dimensione file 744 KB
DOI 10.3280/RSF2024-003008
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Qui sotto puoi vedere in anteprima la prima pagina di questo articolo.

Se questo articolo ti interessa, lo puoi acquistare (e scaricare in formato pdf) seguendo le facili indicazioni per acquistare il download credit. Acquista Download Credits per scaricare questo Articolo in formato PDF

Anteprima articolo

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

Le patologie neurodegenerative associate ai disturbi neuro- cognitivi (DNC) rappresentano una emergenza assistenziale dalle dimensioni epidemiologiche sempre più rilevanti. Tale emergenza è destinata ad aumen- tare in relazione a un progressivo aumento della prospettiva di vita, essendo i DNC correlati con l’età. Se è vero che i DNC si manifestano con una franca alterazione delle funzioni cognitive con impatto funzionale nella vita della persona colpita, è altrettanto vero che il processo patologico inizia, molte volte, prima della comparsa della sindrome clinica. In questo intervallo di tempo, anche a distanza di anni dall’esordio del DNC, possono presentarsi quadri clinici più o meno sfumati di disturbo cognitivo accomunati dall’e- tichetta di “disturbo cognitivo lieve” o Mild Cognitive Impairment (MCI) e di Disturbo Cognitivo Soggettivo o Subjective Cognitive Decline (SCD). Una generale sensibilità a queste forme prodromiche è necessaria per: 1) l’impostazione di un quadro di intervento farmacologico, neuropsicologico ed assistenziale tempestivo ed efficace; 2) l’inserimento della persona in trial clinici per la sperimentazione di farmaci per la stabilizzazione della patolo- gia degenerativa. Ad oggi la distinzione tra invecchiamento normale, forme lievi o soggettive e la loro evoluzione in quadri di DNC maggiori avviene attraverso una attenta anamnesi cognitivo-comportamentale ed una accura- ta valutazione clinica compreso un uso attento e competente degli strumenti cognitivi di screening. Tuttavia, negli ultimi anni, un apporto importante è anche stato offerto dalle tecnologie e nello specifico dall’Intelligenza Artificiale (IA), dal Machi- ne Learning (ML) e dal Deep Learning (DL). Questi strumenti si sono dimo- strati accurati e affidabili: 1) nella rilevazione precoce di un DNC; 2) nella prognosi di una potenziale evoluzione di un DNC lieve in maggiore; 3) nella diagnosi differenziale dei DNC. Il presente lavoro ha l’obiettivo di mostrare in che modo l’applicazione dell’IA, del ML e del DL possa contribuire a una più efficace e tempestiva diagnosi dei disturbi neurodegenerativi.;

Keywords:Disturbi neurocognitivi, Demenza, Intelligenza artificiale, Machine learning, Deep learning

  1. Pompili E. DSM-5-TR: manuale diagnostico e statistico dei distur- bi mentali. American Psychiatric Association. 2023, Accessed: Sep- tember 06, 2024. [Online]. -- https://www.raffaellocortina.it/scheda-li- bro/american-psychiatric-association/dsm-5-tr-edizione-hardcov- er-9788832855173-3925.html.
  2. Grossi D, Trojano L. Lineamenti di neuropsicologia clinica. 2023, ac- cessed: September 06, 2024. -- [Online]. https://www.carocci.it/prodotto/ lineamenti-di-neuropsicologia-clinica-3.
  3. De Langavant LC, Bayen E, Yaffe K. Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study. J Med Internet Res 2018; 20: 7 - DOI: 10.2196/10493
  4. Bayen E, Possin KL, Chen Y, Cleret De Langavant L, Yaffe K. Preva- lence of aging, dementia, and multimorbidity in older adults with Down syndrome. JAMA Neurological 2018; 75; 11: 1399-1406. DOI: 10.1001/JAMANEUROL.2018.2210
  5. Nichols E, Szoeke EL, Abbasi N, Abd-Allah f, Abdela J, et al. Glob- al, regional and national burden of Alzheimer’s disease and other dementias, a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurology 2019; 18; 1: 88-106, DOI: 10.1016/S1474-4422(18)30403-4
  6. Petersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L. Mild cognitive impairment: a concept in evolution. J Intern Med Mar 2014; vol. 275 no. 3: p. 214–228. DOI: 10.1111/JOIM.12190
  7. Petersen RC, Smith E, Waring SC, Ivnik RJ, Tangalos EG, and Kok- men E. Mild cognitive impairment: clinical characterization and out- come. Arch Neurological 1999; vol. 56: no.3: p.303–308, DOI: 10.1001/ARCHNEUR.56.3.303
  8. Reisberg B, Ferris S, De Leon MD, Franssen ESE, Kluger A, Cohen JT, et al. Stage-specific behavioral, cognitive, and in vivo changes in com- munity residing subjects with age-associated memory impairment and primary degenerative dementia of the Alzheimer type. Drug Dev Res Jan. 1988; vol. 15, no. 2–3: p.101–114. DOI: 10.1002/DDR.430150203
  9. Jessen F, Amariglio RE, van Boxter M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia Nov. 2014; vol. 10, no.6: p. 844–852. DOI: 10.1016/J.JALZ.2014.01.001
  10. Glodzik-Sobanska L, Reisberg B, De Santi S, Babb SJ, Pirraglia E, et al. Subjective Memory Complaints: Presence, Severity and Future Out- come in Normal Older Subjects. Dement Geriatric Cognitive Disorder Aug. 2007; vol. 24, no. 3, p: 177–184. DOI: 10.1159/000105604
  11. Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L-O et al. Mild cognitive impairment-beyond controversies, towards a consensus: report of the International Working Group on Mild Cog- nitive Impairment. J Intern Med Sep. 2004 vol. 256, no.3: p. 240–246, DOI: 10.1111/J.1365-2796.2004.01380.X
  12. Fara De Caro M. Modelli e profili neuropsicologici delle patologie neu- rodegenerative. Franco Angeli; 2022.
  13. Limongi F, Siviero P, Noale M, Gesmundo A, Crepaldi G, Maggi S. Prevalence and conversion to dementia of Mild Cognitive Impairment in an elderly Italian population. Aging Clinical Experience Res Ju 2017; vol. 29, no. 3: p. 361–370, DOI: 10.1007/S40520-017-0748-1/METRICS
  14. Ribaldi F, Palomo R, Altomare D, Scheffler M, Assal F, Ashton NJ, et al. The taxonomy of subjective cognitive decline: proposal and first clinical evidence from the Geneva memory clinic cohort. Res Sq Feb. 2023, DOI: 10.21203/RS.3.RS-2570068/V1
  15. Röhr S, Pabst SJ, Riedel-Helen SG, Jessen F, Tirana Y, Handajani YS, et al. Estimating prevalence of subjective cognitive decline in and across international cohort studies of aging: a cosmic study. Alzheimers Res Therapy Dec. 2020; vol. 12, no. 1.
  16. Bessi V, Mazzeo S, Padiglioni S, Piccini C, Nacmias B, Sorbi S, Bracco L. From Subjective Cognitive Decline to Alzheimer’s disease: The Pre- dictive Role of Neuropsychological Assessment, Personality Traits, and Cognitive Reserve. A 7-Year Follow-Up Study. Journal of Alzheimer’s disease 2018; vol. 63, no. 4: p. 1523–1535. DOI: 10.3233/JAD-171180
  17. Dolcet-Negre MM, Imaz Aguayo L, García-de-Eulate R, Martí-Andrés G, Fernández-Matarrubia M, Domínguez P, et al. Predicting Conver- sion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer’s Disease Dementia Using Ensemble Machine Learn- ing. J Alzheimers Disease May 2023; vol. 93, no. 1: p. 125–140. DOI: 10.3233/JAD-221002
  18. Sabbagh MN, Boada M, Borson S, Chilukuri M, Doraiswamy PM, Dubois B, et al. Rationale for Early Diagnosis of Mild Cognitive Im- pairment (MCI) Supported by Emerging Digital Technologies. J Prev Alzheimers Disease Mar 2020; vol.7, no.3: p.158–164. DOI: 10.14283/JPAD.2020.19
  19. Boccardi M, Nicolosi V, Festari C, Bianchetti A, Cappa S, Chiasserini D, et al. Italian consensus recommendations for a biomarker-based eti- ological diagnosis in mild cognitive impairment patients. Eur J Neuro- logical Mar. 2020; vol.27, no.3: p.475–483. DOI: 10.1111/ENE.14117
  20. Jiménez-Huete A, Villino-Rodríguez R, Ríos-Rivera MM, Rognoni Montoya-Murillo G, Arrondo C, et al. Clusters of cognitive perfor- mance predict long-term cognitive impairment in elderly patients with subjective memory complaints and healthy controls. Alzheimer’s & De- mentia Jul. 2024; vol.20, no.7: p. 4702–4716. DOI: 10.1002/ALZ.13903
  21. Slegers A, Chafouleas G, Montembeault M, Bedetti C, Welch AE, Brambati SM et al. Connected speech markers of amyloid burden in primary progressive aphasia. Cortex Dec.2021 vol.145: p.160–168. DOI: 10.1016/J.CORTEX.2021.09.010
  22. Szatloczki G, Hoffmann I, Vincze V, Kalman J, Pakaski M. Speaking in Alzheimer’s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer’s disease. Front Aging Neurosci- ence Oct. 2015; vol. 7. DOI: 10.3389/FNAGI.2015.00195
  23. Kim J, Jang H, Park YH, Youn J, Seo SW, Kim HJ, Na DL. Motor Symptoms in Early- versus Late-Onset Alzheimer’s disease. J Alzhei- mers Disease 2023; vol.91, no.1: pp. 345–354. DOI: 10.3233/JAD-220745
  24. Tangen GG, Nilsson MH, Stomrud E, Palmqvist S, Hansson O. Spatial Navigation and Its Association with Biomarkers and Future Dementia in Memory Clinic Patients Without Dementia. Neurology Nov. 2022; vol. 99: no. 19: p. E2081. DOI: 10.1212/WNL.0000000000201106
  25. Pascarella A, Manzo L, and Ferlazzo E. Modern neurophysiological techniques indexing normal or abnormal brain aging. Seizure: Europe- an Journal of Epilepsy, Jul. 2024, DOI: 10.1016/J.SEIZURE.2024.07.001
  26. Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C. Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare Apr. 2019; vol.7, no.2: p.60.
  27. Qiu S, Miller MI, Joshi P, Lee JC. Multimodal deep learning for Alz- heimer’s disease dementia assessment. Nat Commun Dec. 2022; vol. 13, no. 1.
  28. Kang MJ, Kim SY, Na DL, Kim BC. Prediction of cognitive impair- ment via deep learning trained with multi-center neuropsychological test data. BMC Med Inform Decision Making Nov. 2019; vol. 19.
  29. Basta M, Simos NJ, Zioga M, Zaganas I, Panagotakis S, Lionis C, Vgontzas AN. Personalized screening and risk profiles for Mild Cog- nitive Impairment via a Machine Learning Framework: Implications for general practice. Int J Med Inform Feb.2023; vol. 170.
  30. Wang T, Hong Y, Wang Q, Su R, Ng ML, su J, et al. Identification of Mild Cognitive Impairment among Chinese Based on Multiple Spoken Tasks. Journal of Alzheimer’s Disease 2021; vol. 82, no. 1, pp. 185–204.
  31. Al Harkan K, Sultana N, Al Mulhim N, AlAbdulKader AM, Al- safwani N, Barnawi M, Alasqah K, et al. Artificial intelligence ap- proaches for early detection of neurocognitive disorders among older adults. Front Computer Neuroscience 2024; vol. 18.
  32. Xue C, Kowshik SS, Lteif D, Puducheri S, Jasodanand VH, Zhou OT, et al. AI-based differential diagnosis of dementia etiologies on multi- modal data. Nat Med, 2024,
  33. Perovnik M, Vo A, Nguyen N, Jamsek J, Rus T, Tang CC, et al. Auto- mated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neuroscience Nov. 2022; vol. 14.
  34. Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia. Fed by MRI Select- ed Features, Front Neuroinformation Jun. 2020; vol. 14.
  35. Brzezicki MA, Kobetić MD, Neumann S, Pennington C. Diagnostic ac- curacy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement. Adv. Med Science Sep. 2019; vol. 64, no. 2, pp. 292–302.
  36. Peng B, Yao X, Risacher SL, Saykin AJ, Shen L, Ning X. Cognitive biomarker prioritization in Alzheimer’s Disease using brain morpho- metric data. BMC Med Inform Decision Making Dec. 2020; vol. 20, no. 1.
  37. Garcia-Gutierrez F, Delgado-Alvarez A, Delgado-Alonso C, Díaz-Ál- varez J, Pytel, Valles-Salgado M, et al. Diagnosis of Alzheimer’s dis- ease and behavioral variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineer- ing and genetic algorithms. Int J Geriatric Psychiatry Dec. 2022; vol. 37, no.2.
  38. Moradi E, Hallikainen I, Hänninen T, Tohka J. Rey’s Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alz- heimer’s disease. Neuroimage Clinical 2017; vol. 13: p.415–427.
  39. Fristed E. Leveraging speech and artificial intelligence to screen for early Alzheimer’s disease and amyloid beta positivity. Brain Commu- nication 2022; vol. 4, no. 5.
  40. Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment clas- sification: A machine learning study. Behavioral Neurology 2017; vol. 2017. DOI: 10.1155/2017/1850909
  41. Fayemiwo MA. Immediate word recall in cognitive assessment can predict dementia using machine-learning techniques. Alzheimers Res Therapy 2023; vol. 15, no. 1.
  42. Quek LJV, Heikkonen MR, Lau Y. Use of artificial intelligence tech- niques for detection of mild cognitive impairment: A systematic scop- ing review. J Clinical Nursery Sep. 2023; vol. 32; no. 17–18: p.5752– 5762. DOI: 10.1111/JOCN.16699
  43. Vrahatis AG, Skolariki K, Krokidis MG, Lazaros K, Exarchos TP, Vlamos P. Revolutionizing the Early Detection of Alzheimer’s Dis- ease through Non-Invasive Biomarkers: The Role of Artificial Intelli- gence and Deep Learning. Sensors (Basel) May 2023; vol. 23, no. 9. DOI: 10.3390/S23094184
  44. Formica C. Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders. J Pers Med Sep. 2023; vol. 13, no. 9.
  45. Georgakis MK. Validation of TICS for detection of dementia and mild cognitive impairment among individuals characterized by low levels of education or illiteracy: a population-based study in rural Greece. Clinical Neuropsychology Nov. 2017; vol. 31: p.61–71. DOI: 10.1080/13854046.2017.1334827
  46. Amini S. An Artificial Intelligence-Assisted Method for Dementia De- tection Using Images from the Clock Drawing Test. Journal of Alzhei- mer’s Disease 2021; vol. 83, no. 2: p.581–589. DOI: 10.3233/JAD-210299
  47. Binaco R. Machine learning analysis of digital clock drawing test per- formance for differential classification of mild cognitive impairment subtypes versus Alzheimer’s disease. Journal of the International Neu- ropsychological Society Aug. 2020; vol. 26, no. 7: p. 690–700. DOI: 10.1017/S1355617720000144
  48. Umegaki H. Association of the Qualitative Clock Drawing Test with Progression to Dementia in Non-Demented Older Adults. J Clinical Med Sep. 2020; vol. 9, no. 9: p.1–9. DOI: 10.3390/JCM9092850
  49. Kim S, Jahng S, Yu KH, Lee BC, Kang Y. Usefulness of the Clock Drawing Test as a Cognitive Screening Instrument for Mild Cognitive Impairment and Mild Dementia: an Evaluation Using Three Scoring Systems. Dement Neurocognitive Disorder 2018; vol. 17, no. 3: p.100. DOI: 10.12779/DND.2018.17.3.100
  50. Cacho J, Benito-León J, García-García R, Fernández-Calvo B, Vicen- te-Villardón JL, Mitchell AJ. Does the combination of the MMSE and clock-drawing test (mini-clock) improve the detection of mild Alzhei- mer’s disease and mild cognitive impairment? J Alzheimers Disease 2010; vol. 22, no. 3: p. 889–896. DOI: 10.3233/JAD-2010-101182
  51. Binaco R. Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Im- pairment Subtypes Versus Alzheimer’s Disease. J Int Neuropsycho- logical Society Aug. 2020; vol. 26, no. 7: p. 690–700. DOI: 10.1017/S1355617720000144
  52. Souillard-Mandar W. Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test. Mach Learn Mar. 2016; vol. 102, n. 3: p. 393–441. DOI: 10.1007/S10994
  53. 015-5529-5.
  54. Jiménezjim C. Using XAI in the Clock Drawing Test to reveal the cog- nitive impairment pattern 2021.

Raffaele Nappo, Roberta Simeoli, Il contributo dell’intelligenza artificiale nella diagnosi dei disturbi neurodegenerativi in "RIVISTA SPERIMENTALE DI FRENIATRIA" 3/2024, pp 131-144, DOI: 10.3280/RSF2024-003008