Search results for: DATSCAN

Authors: Shraddha Gopal, Jayam Lazarus

Abstract:

Aims: To study the referral practice and impact of DAT-scan in the diagnosis or exclusion of Parkinson’s disease. Settings and Designs: A retrospective study Materials and methods: A retrospective study of the results of 60 patients who were referred for a DAT scan over a period of 2 years from the Department of Neurology at Northern Lincolnshire and Goole NHS trust. The reason for DAT scan referral was noted under 5 categories against Parkinson’s disease; drug-induced Parkinson’s, essential tremors, diagnostic dilemma, not responding to Parkinson’s treatment, and others. We assessed the number of patients who were diagnosed with Parkinson’s disease against the number of patients in whom Parkinson’s disease was excluded or an alternative diagnosis was made. Statistical methods: Microsoft Excel was used for data collection and statistical analysis, Results: 30 of the 60 scans were performed to confirm the diagnosis of early Parkinson’s disease, 13 were done to differentiate essential tremors from Parkinsonism, 6 were performed to exclude drug-induced Parkinsonism, 5 were done to look for alternative diagnosis as the patients were not responding to anti-Parkinson medication and 6 indications were outside the recommended guidelines. 55% of cases were confirmed with a diagnosis of Parkinson’s disease. 43.33% had Parkinson’s disease excluded. 33 of the 60 scans showed bilateral abnormalities and confirmed the clinical diagnosis of Parkinson’s disease. Conclusion: DAT scan provides valuable information in confirming Parkinson’s disease in 55% of patients along with excluding the diagnosis in 43.33% of patients aiding an alternative diagnosis.

Keywords: DATSCAN, Parkinson's disease, diagnosis, essential tremors

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

Abstract:

Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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