RT Journal Article SR Electronic T1 Differential diagnosis of parkinsonism based on deep metabolic imaging indices JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP jnumed.121.263029 DO 10.2967/jnumed.121.263029 A1 Ping Wu A1 Yu Zhao A1 Jianjun Wu A1 Matthias Brendel A1 Jiaying Lu A1 Jingjie Ge A1 Alexander Bernhardt A1 Ling Li A1 Ian Alberts A1 Sabrina Katzdobler A1 Igor Yakushev A1 Jimin Hong A1 Qian Xu A1 Yimin Sun A1 Fengtao Liu A1 Johannes Levin A1 Günter Höglinger A1 Claudio Bassetti A1 Yihui Guan A1 Wolfgang H Oertel A1 Wolfgang Andreas Weber A1 Axel Rominger A1 Jian Wang A1 Chuantao Zuo A1 Kuangyu Shi YR 2022 UL http://jnm.snmjournals.org/content/early/2022/04/07/jnumed.121.263029.abstract AB The clinical presentations of early idiopathic Parkinson’s disease (PD) substantially overlap with those of atypical parkinsonian syndromes like multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1275 parkinsonian patients and 863 non-parkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3D deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices, which was blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI, and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8% for the diagnosis of PD, MSA, and PSP in the blind test cohort. In the German cohort, They resulted in sensitivities of 94.1%, 82.4%, 82.1%, and specificities of 84.0%, 99.9%, 94.1% respectively. Employing the PET scans independently achieved comparable performance to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show potential to provide an early and accurate differential diagnosis for parkinsonism and is robust when dealing with discrepancies between populations and imaging acquisitions.