Published online Jun 28, 2020. doi: 10.35711/aimi.v1.i1.65
Peer-review started: March 17, 2020
First decision: June 5, 2020
Revised: June 15, 2020
Accepted: June 18, 2020
Article in press: June 18, 2020
Published online: June 28, 2020
Processing time: 114 Days and 14.2 Hours
Diagnosis of a dementia subtype can be complex and often requires comprehensive cognitive assessment and dedicated neuroimaging. Clinicians are prone to cognitive biases when reviewing such images. We present a case of cognitive impairment and demonstrate that initial imaging may have resulted in misleading the diagnosis due to such cognitive biases.
A 76-year-old man with no cognitive impairment presented with acute onset word finding difficulty with unremarkable blood tests and neurological examination. Magnetic resonance imaging (MRI) demonstrated multiple foci of periventricular and subcortical microhaemorrhage, consistent with cerebral amyloid angiopathy (CAA). Cognitive assessment of this patient demonstrated marked impairment mainly in verbal fluency and memory. However, processing speed and executive function are most affected in CAA, whereas episodic memory is relatively preserved, unlike in other causes of cognitive impairment, such as Alzheimer’s dementia (AD). This raised the question of an underlying diagnosis of dementia. Repeat MRI with dedicated coronal views demonstrated mesial temporal lobe atrophy which is consistent with AD.
MRI brain can occasionally result in diagnostic overshadowing, and the application of artificial intelligence to medical imaging may overcome such cognitive biases.
Core tip: This case represents the complexities of diagnosing dementia subtypes with an unusual presentation for what is likely Alzheimer’s dementia, rather than cerebral amyloid angiopathy as per initial magnetic resonance imaging brain. In such cases, imaging can potentially influence the diagnostic accuracy, which might ultimately result in misdiagnosis and hence alter the management plan. We argue that artificial intelligence and image automation could avoid such diagnostic oversights.