MF, Centralna medicinska knjižnica, Ljubljana (CMK)
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Automated differential diagnosis of dementia syndromes using FDG PET and machine learning [Elektronski vir]Perovnik, Matej, 1992- ...Background: Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative ... dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. Methods: We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer’s disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients’ clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer’s disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multiclass support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. Results: Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. Conclusion: Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.Vir: Frontiers in aging neuroscience [Elektronski vir]. - ISSN 1663-4365 (Vol. 14, iss. , [article no.] 1005731, 2022, str. 1-12)Vrsta gradiva - e-članek ; neleposlovje za odrasleLeto - 2022Jezik - angleškiCOBISS.SI-ID - 130199043
Avtor
Perovnik, Matej, 1992- |
Vo, An |
Nguyen, Nha |
Jamšek, Jan, dr. med. |
Rus, Tomaž, dr. med. |
Tang, Chris Chengke |
Trošt, Maja |
Eidelberg, David
Teme
dementia |
differential diagnosis |
visual reading |
machine learning |
FDG PET |
demenca |
diferencialna diagnostika |
vizualno branje |
strojno učenje |
FDG PET
Vnos na polico
Trajna povezava
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Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Perovnik, Matej, 1992- | 51885 |
Vo, An | |
Nguyen, Nha | |
Jamšek, Jan, dr. med. | 38537 |
Rus, Tomaž, dr. med. | 51578 |
Tang, Chris Chengke | |
Trošt, Maja | 15442 |
Eidelberg, David |
Vir: Osebne bibliografije
in: SICRIS
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