**4.5 Intracranial aneurysms**

Intracranial aneurysms (IAs) are commonplace in the population, with a global estimated prevalence between 2 and 5% [78]. Although most of these aneurysms are asymptomatic, they carry the risk of rupture which if realized leads to a subarachnoid hemorrhage – a prognosis producing a dramatic case fatality of 50% [79]. Thus, there is great interest in the rapid and accurate identification of unruptured intracranial aneurysms on brain imaging.

At the present moment, intra-arterial digital subtraction angiography (IADSA) is the gold-standard for the diagnosis of intracranial aneurysms, with computed tomography angiography (CTA), magnetic resonance angiography (MRA), and transcranial Doppler sonography also shown to be effective diagnostic tests [80]. Time-of-flight MR angiography (TOF-MRA) is a non-invasive, non-contrast enhanced technique that enables discrimination between vessels and stationary tissues by inducing blood inflow effects [81]. Due to the absence of ionizing radiation or intravenous contrast agents, time-offlight MR angiography (TOF-MRA) is typically the first modality of choice for aneurysm screening. Hence, many inroads for DL applications have been explored in this area.

Nakao et al. developed a computer-assisted detection (CAD) deep CNN architecture combined with a maximum intensity projection (MIP) algorithm trained on 450 patients worth of TOF-MRA scans. The team achieved a high sensitivity of 94.2% (98/104) and only 2.9 false positives per case [82]. Faron et al. similarly developed a CNN model finding an overall sensitivity of 90% with a false positive rate of 6.1%. More consequently, the Faron team further found that there was no significant difference in aneurysm detection performance between the CNN model and two blinded diagnostic neuroradiologists, with an overall increase in human detection sensitivity when combining their detection hits with the CNN model's hits (reader 1: 98% vs. 95%, P = 0.280; reader 2: 97% vs. 94%, P = 0.333) [83].

Ueda et al. developed a ResNet architecture algorithm fed with 683 TOF-MRA patient scans and achieved a sensitivity of 91% (592 of 649) and 93% (74 of 80) for their internal and external data sets, respectively [84]. More interestingly, the model improved aneurysm detection in their retrospectively collected TOF-MRA scans by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared to the initial radiologistinterpreted assessments.

Until recently, machine-learning algorithms largely focused on MRA imaging. However, more recent efforts were expanded to include CT-based imaging
