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A High Rate of Human Error in Early Detection of Small Brain Metastases Suggests a Basis for Development of Artificial Intelligence Recognition Technology

JSHS · 2024

Isabella Zhang, Shuo Wang · Millard North High School, Omaha, NE

Overview

Brain Metastases carry poor prognosis in cancer patients with their rapidly growing nature. Early identification of these tumors is crucial in improving patient survival. This investigation aims to study the rate of human error in missing early brain metastases and factors associated with the threshold of sensitivity of human eyes. Data from the University of Nebraska Medical Center was used. The database included patients with new brain metastasis diagnosed based on brain magnetic resonance imaging (MRI) who also had previous MRI scan(s) 1-6 months before diagnosis and no exposure to whole-brain radiotherapy. The brain MRI used for diagnosis of brain metastasis and the MRI performed 1 -6 months prior were reviewed. Based on the location of the newly diagnosed tumor, the corresponding location in the previous MRI was assessed for a missed incidence of a preexisting tumor. The sizes of the missed tumors were then measured to assess the threshold of human eyes in detecting metastases. The percentage of missed metastases was 44% (56/126). The mean size of missed metastases was 3.0 millimeters (range 1.2 to 7.7 mm). No clinical factors were significantly associated with a higher rate of missed diagnosis. The most likely reason for the missed diagnosis is the tiny size although visual distraction seems to play a role including adjacent contrast-enhancing structures such as blood vessels. The results show a high rate of human error for missing small metastases. These results justify the development of artificial intelligence-based recognition to assist neuroradiologists in diagnosis.

Competition history

  • JSHS 2024 Category not listed

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Source: Junior Science and Humanities Symposium

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