Neurological disorders are responsible for the highest rate of disability and the second-highest rate of mortality globally (Feigin et al., 2020). Medical imaging in neurology mostly relies on modalities that generate large amounts of complex data including magnetic resonance imaging (MRI), computed tomography (CT), and nuclear imaging. Thus, a large amount of research into artificial intelligence (AI) applications in radiology has targeted neurological disorders. In fact, between 29% and 38% of all commercially available AI-based applications in radiology focus on the brain or spine, a higher proportion than for any other anatomical region (AI Central).
Most of these applications aim to help radiologists by either supporting their interpretation of images, for example, by making these tasks more efficient or by extending radiologists’ capabilities such as by providing more detailed quantification of neuroimaging data (Olthof et al., 2020). This book outlines the most common applications of AI in neuroradiology and discusses the evidence behind them.
Acute intracranial hemorrhage (ICH) affects about 3.4 million people every year worldwide (world stroke organisation 2022). ICH carries high morbidity and mortality and often requires prompt neurosurgical intervention or close clinical and imaging follow-up (Broderick et al., 2007; van Asch et al., 2010). Particularly in patients presenting with acute neurological deficits and suspected of having a stroke, the detection of acute intracranial hemorrhage is of paramount importance as it is an absolute contraindication to intravenous thrombolysis (Fugate & Rabinstein, 2015).
In the emergency setting, suspected cases of ICH are usually initially investigated using non-contrast CT (NCCT) of the head. This is because CT is widely available, quick, highly sensitive for ICH, and has relatively few contraindications (A. Jain et al., 2021). The alternative is MRI, which is more sensitive to very small and chronic hemorrhages but is slower, less readily available, more expensive, and contraindicated in some patients (Chalela et al., 2007).
In a study aimed to determine patterns of error by radiology residents in detecting ICH, researchers found discrepancies in 4.6% of the resident-interpreted overnight examinations and of that percentage 13.6% were due to hemorrhage that was not included or inaccurately reported in the residents’ report. (Strub et al., 2007). ICH can be subdivided into intraparenchymal hemorrhage, intraventricular hemorrhage, subdural hemorrhage, extradural hemorrhage, and subarachnoid hemorrhage. Of these, subdural and subarachnoid hemorrhages are the commonly missed, particularly if very small (Strub et al., 2007). In addition, normal brain anatomy and image artifacts are often mistaken for intracranial hemorrhage by reporting radiology residents (Erly et al., 2002).
The vast majority of AI-based applications aiming to detect and classify intracranial hemorrhage use NCCT as an input and are based on convolutional neural networks. With few exceptions (Bar et al., 2019; Wang et al., 2021; Ye et al., 2019), very detailed descriptions of the network architecture are not readily available for most applications. The amount and quality of the data used to train these algorithms varies widely, from hundreds (Bar et al., 2019; Heit et al., 2021) to thousands (McLouth et al., 2021; Rava, Seymour, et al., 2021) to tens of thousands (Chilamkurthy et al., 2018; Gibson et al., 2022; Ginat, 2021) of NCCT examinations.
In addition to the classification of the presence or absence of ICH, AI-based algorithms applications have also been used to classify ICH subtypes (Chilamkurthy et al., 2018; Gibson et al., 2022; Wang et al., 2021; Ye et al., 2019), detect associated findings like mass effect, midline shift, and fractures (Chilamkurthy et al., 2018), and perform hemorrhage segmentation and volumetry (Bar et al., 2019; Gibson et al., 2022; Heit et al., 2021). Additionally, one AI-based application also estimates the degree of uncertainty in the algorithm’s decision to help the radiologist interpret the algorithm’s output (Gibson et al., 2022).
Among the subtypes of ICH, AI-based applications from the studies mentioned generally show the highest sensitivity for intraventricular hemorrhage (Chilamkurthy et al., 2018; Gibson et al., 2022; McLouth et al., 2021; Wang et al., 2021), most likely because of the large difference in CT density between cerebrospinal fluid and blood. Across applications, sensitivity is relatively low for subarachnoid hemorrhages (Gibson et al., 2022; McLouth et al., 2021; Rava, Seymour, et al., 2021; Wang et al., 2021; Ye et al., 2019), possibly because these tend to be small and/or adjacent to bony structures or hyperdense CT artifacts (e.g. in the basal cisterns). Other applications have also shown relatively low sensitivity for subdural hemorrhage, particularly when in less common locations such as along the cerebral falx (Chilamkurthy et al., 2018; Rao et al., 2021; Wang et al., 2021; Ye et al., 2019). Sensitivity also tends to be lower for smaller hemorrhages, defined as <1.5 mL or <5 mL, depending on the study (Heit et al., 2021; McLouth et al., 2021; Rava, Seymour, et al., 2021). Only one of the studies mentioned have systematically investigated differences between scanner vendors and scanning parameters on the diagnostic performance of AI-based applications for ICH detection (McLouth et al., 2021).
Some studies have directly compared the AI-based applications’ performance to that of experts. In a study of 160 NCCTs (49% with ICH) using a neuroradiology consultant’s assessment as ground truth, a U-Net convolutional neural network (CNN) showed lower sensitivity (91%) and specificity (89%) compared to two neuroradiology residents (99-100% sensitivity and 98% specificity)(Schmitt et al., 2022). In another study, interpretations from a FDA-approved and CE-marked AI-based application were compared with readings from a panel of three attending neuroradiologists that defined ground truth.
The AI-based application demonstrated the same sensitivity as a neuroradiology fellow (91.9%), however the application’s specificity was substantially lower (application: 84.4%; fellow: 99.6%)(Eldaya et al., 2022). Another AI-based application had a higher sensitivity and slightly lower specificity for ICH than radiology trainees (Ye et al., 2019). Dural thickening, dural and intraparenchymal calcifications, and motion or streak artifacts are most likely to be mistaken for ICH by AI-based applications (Bar et al., 2019; Eldaya et al., 2022; Rao et al., 2021).
Many studies have investigated the diagnostic accuracy AI-based applications for detecting ICH, however another potential benefit to AI-based screening for ICH is that exams can be read faster which may lead to patients being managed more quickly. Although fewer studies have evaluated the impact AI-based screening has had on timing, some studies provide support for faster reading times. In a study of 620 NCCTs, the time from exam completion to reporting was 73 minutes when the AI notified the human reader that it had found something, as opposed to 132 minutes when no such notification took place (Wismüller & Stockmaster, 2020). In another study, using the AI-based application was associated with shorter patient stays in the emergency department (561 minutes vs 781 minutes without the AI) (Chien et al., 2022).
Acute ischemic stroke
Large vessel occlusion
In patients with acute ischemic stroke, quickly identifying occlusions of large vessels in the brain is essential for timely treatment. In general, the term “large vessel occlusion (LVO)” refers to occlusions of arteries large enough to be amenable to mechanical thrombectomy. Currently, this includes the internal carotid artery (ICA)m the proximal parts of the middle (M1 and M2), anterior (A1), and posterior (P1) cerebral arteries, as well as the basilar artery (Mokin et al., 2019; Pirson et al., 2022).
LVOs are either detected directly using digital subtraction angiography, CT angiography, or MR angiography or indirectly using non-angiographic techniques. On angiography, vessel occlusions appear as a sudden interruption of either contrast filling of an artery (in contrast-enhanced angiography) or flow signal (in non-contrast-enhanced techniques such as time-of-flight MR angiography). This can occur with or without the presence of contrast filling or flow signal distal to the occlusion site. Indirect imaging signs of LVO on non-angiographic techniques include a hyperdense vessel on NCCT (representing the occluding thrombus) (Gács et al., 1983) and a susceptibility thrombus sign on T2*- or susceptibility-weighted MR images (Flacke et al., 2000).
Most AI-based solutions for LVO detection use CT angiography (Amukotuwa et al., 2019; Murray et al., 2020; Rava, Peterson, et al., 2021; Wardlaw et al., 2022; Yahav-Dovrat et al., 2021), while others use NCCT (Lisowska et al., 2017; Olive-Gadea et al., 2020). Most applications have focused on LVOs of the intracranial arteries of the anterior circulation (Adhya et al., 2021; Amukotuwa et al., 2019; Dehkharghani et al., 2021; Rava, Peterson, et al., 2021), reflecting the fact that mechanical thrombectomy is much less commonly performed in posterior circulation vessel occlusions (Adusumilli et al., 2022).
In a review of evidence on AI-based applications for detecting LVO sensitivities ranged from 80-96% and specificities ranging from 90-98% (Wardlaw et al., 2022). False positives from the studies included in the review of evidence were commonly due to arterial stenosis, intracranial hemorrhage, hypervascular tumors, or distal vessel occlusions that do not fulfill the criteria of an LVO (Amukotuwa et al., 2019; Yahav-Dovrat et al., 2021). Unfortunately, published performance data are not available for a number of CE-marked AI-based solutions, including some designed for LVO detection (van Leeuwen et al., 2021).
At the time of writing this publication, there is only one study available that investigated the costeffectiveness of AI-based tools for LVO detection. The study’s analysis showed that, assuming that 6% of LVOs are missed by clinicians and AI can help reduce that by half, cost savings of $11 million per year could be achieved in the United Kingdom (van Leeuwen, Meijer, et al., 2021).
Because LVOs are not commonly missed on angiographic techniques by radiologists and radiology residents (Duvekot et al., 2021), the primary potential benefit of AI-based LVO detection is expediting treatment by providing a faster assessment. Some of the currently available applications require between about 1 and 3.5 minutes to process the data and reach a decision regarding the presence of an LVO (Amukotuwa et al., 2019; Dehkharghani et al., 2021; Olive-Gadea et al., 2020). Some tools have been associated with a reduction in the time from imaging to patient transfer to a hospital capable of performing mechanical thrombectomy by about 22.5 minutes (Hassan et al., 2020), the time from the patient’s arrival to the hospital to notification of the neuroendovascular team by about 15 minutes (Morey et al., 2021), and the time from imaging to groin puncture for mechanical thrombectomy by about 25 minutes (Adhya et al., 2021).
Early ischemic brain tissue changes
On CT, early brain tissue changes associated with ischemia include tissue swelling and reduced tissue attenuation due to ionic edema (Marks et al., 1999). These changes are incorporated into visual rating tools used by radiologists, the most common being the Alberta Stroke Program Early CT Score (ASPECTS). ASPECTS can help predict both functional outcomes and the development of symptomatic intracranial hemorrhage after intravenous thrombolysis (Schröder & Thomalla, 2016). Most AI-based applications aiming to detect early ischemic changes on NCCT do so by providing an automated assessment of ASPECTS (Wardlaw et al., 2022). Other applications aim to identify early ischemic changes using CT-angiography (Abdelkhaleq et al., 2021; Öman et al., 2019) or CT-perfusion (Hakim et al., 2021).
The majority of AI-based algorithms for identifying early ischemic changes on CT have used visual assessment of NCCT by radiologists, neuroradiologists, or other clinicians as a reference standard (Goebel et al., 2018; Hoelter et al., 2020; Kniep et al., 2020; Maegerlein et al., 2019; Seker et al., 2019), while some have used MRI diffusion-weighted imaging (Abdelkhaleq et al., 2021; Herweh et al., 2016; H. Kuang et al., 2019; Qiu et al., 2020) or the infarct core defined by CT perfusion (Olive-Gadea et al., 2019). Most of these applications use either random forests (Guberina et al., 2018; Herweh et al., 2016; Kniep et al., 2020; H. Kuang et al., 2019; Maegerlein et al., 2019; Nagel et al., 2017; Olive-Gadea et al., 2019; Qiu et al., 2020) or convolutional neural networks (Öman et al., 2019). In addition, many studies have focused on the automated identification of early ischemic changes on diffusion-weighted MRI (Boldsen et al., 2018; Mohd Saad et al., 2019; Nazari-Farsani et al., 2020; Siddique et al., 2022; Song, 2019; Wong et al., 2022), which is a highly sensitive but not widely available method in acute settings.
Similar to LVO applications, publicly available performance data is unavailable for some CE-marked AI-based solutions for the detection of early ischemic changes (van Leeuwen et al., 2021). The algorithm for which the most published data are available is a random forest approach to ASPECTS assessment that was found to be non-inferior to neuroradiologists with a sensitivity of 44% and specificity of 93% using follow-up CT as ground truth (Nagel et al., 2017). Another study using the same algorithm and ground truthing method found that the algorithm had a higher sensitivity (83% vs 73%) but lower specificity (57% vs 84%) for ASPECTS scoring compared to neuroradiologists (Guberina et al., 2018). In a third study, this algorithm also performed better at ASPECTS scoring compared to neurologists and neurology residents, and performed similarly in comparison to neuroradiologists (Ferreti et al., 2020).
Overall, few studies have directly compared different AI-based applications for detecting early ischemic changes on NCCT (Goebel et al., 2018; Hoelter et al., 2020). In one study, three commercially available applications (two based on machine learning and one based on densitometry) were compared in 131 patients (Hoelter et al., 2020).
The study found that the AI-based applications had an area under the curve (AUC) of between 0.73 and 0.76 compared to the consensus of three neuroradiologists.
Visual evaluation of early ischemic changes on NCCT is particularly difficult in the posterior fossa, where artifacts are common and hinder interpretability (Hwang et al., 2012). In a cohort of 69 patients with basilar artery occlusions who received a NCCT within 6 hours of symptom onset, a random-forest-based algorithm identified early ischemic changes in the posterior circulation with an AUC ranging from 0.70 (in the cerebellum) to 0.82 (in the thalamus) using follow-up NCCT as ground truth (Kniep et al., 2020). Several factors besides anatomic location influence the detectability of early ischemic changes on NCCT. One study found that the accuracy of ASPECTS assessment differs according to the type of CT reconstruction used, but an automated algorithm performed more consistently across several investigated CT reconstructions than radiology residents or consultants (Seker et al., 2019). In addition, the accuracy of both human and AI-based ASPECTS assessments increases with longer time from symptom onset to NCCT due to early ischemic changes becoming more pronounced (Potreck et al., 2022).
Strokes of unknown onset time
Knowing how long it has been since stroke symptoms started is crucial for guiding appropriate treatment because intravenous thrombolysis is only indicated when given within 4.5 hours of symptom onset (Powers et al., 2018). Stroke onset is not always definitive, for instance in patients presenting with wake-up stroke. Wake-up stroke occurs in approximately 14% of patients as reported in a population-based study performed on patients presenting to an emergency department (Mackey et al., 2011). Several imagingbased approaches to identifying patients within the thrombolysis time window have been proposed. One thoroughly investigated approach thus far has been the presence of an acute stroke lesion on diffusion-weighted imaging (DWI) and its absence on fluid-attenuated inversion recovery (FLAIR) MRI. (Ebinger et al., 2010; Thomalla et al., 2011; Thomalla et al., 2018). Automated interpretation of DWI and FLAIR MRI images have also become a target of AI-based algorithms designed to assist radiologists.
Approaches to AI-based classification of stroke onset times have included CNNs (Polson et al., 2022) or a combination of different machine-learning algorithms (Jiang et al., 2022; H. Lee et al., 2020; Zhu et al., 2021). Some studies have used a radiomics-based approach involving segmenting DWI and FLAIR lesions, extracting different imaging features from them, and then feeding these features to different classification algorithms (Jiang et al., 2022; H. Lee et al., 2020; Zhu et al., 2021).
AI-based classification of stroke onset times has yielded higher sensitivities but lower specificities than visual assessment by radiologists in several studies (H. Lee et al., 2020; Polson et al., 2022). Sensitivities ranging from 73-86% and specificities ranging from 68-85% have been reported (Jiang et al., 2022; H. Lee et al., 2020; Polson et al., 2022; Zhu et al., 2021). A study using a radiomics-based approach based on only the DWI and T1-weighted images combined with a deep-learning algorithm found a sensitivity of 95% and a specificity of 50% for identifying patients within the thrombolysis time window (Y.-Q. Zhang et al., 2022).
Traumatic brain injury
Acute traumatic brain injury (TBI) is a sudden physical trauma that damages the brain. Its manifestations include ICH, diffuse axonal injury, and skull and facial fractures. In addition, consequences of some of these manifestations such as midline shift and brain herniation, which can require emergency treatment if severe, can be detected on imaging (Schweitzer et al., 2019).
Though, undisplaced skull fractures without associated ICH are treated conservatively (Skull Fractures, n.d.), few studies have addressed their detection using AI-based techniques. Nonetheless, some attempts have recently been made to classify skull fractures detected on NCCT.
An algorithm based on a multi-label learning approach and trained on 174 NCCTs (103 with fractures) showed a 98% precision and 92% specificity for detecting skull fractures (Emon et al., 2022). The lowest precision and specificity were for depressed fractures, and the highest precision and specificity were for linear fractures and facial fractures. A deep-learning-based application aimed at detecting critical findings on non-contrast head CT showed a sensitivity of 81.2-87.2% and a specificity of 77.5-86.1% (depending on the test dataset) for detecting skull fractures (Chilamkurthy et al., 2018). In the same study, midline shift and mass effect, both common consequences of trauma-related ICH, were identified with a sensitivity of 87.5-90.1% and 70.9-81.2% as well as a specificity of 83.7-89.4% and 61.6-73.4% (depending on the test dataset), respectively. An algorithm that combined extraction of the skull’s morphological features with CNNs and was trained on 25 NCCTs and tested on 10 NCCTs from head trauma patients had an average precision of 60% for detecting skull fractures (Z. Kuang et al., 2020). Another deep learning algorithm was 91.4% sensitive and 87.5% specific in identifying skull fractures in a series of 150 postmortem head CTs (Heimer et al., 2018).
Many neurological conditions can be described as neurodegenerative, but the term is usually used to refer to chronic neurological diseases associated with gradual loss of brain tissue and generally causing dementia and/or motor dysfunction (Lamptey et al., 2022). More than a fifth of CE-certified or FDA-cleared AI-based algorithms in neuroradiology target patients with dementia (AI for Radiology, n.d.). Most of these automatically calculate regional brain volumes, measure cortical thickness, and quantify white matter lesions caused by cerebral small vessel disease (AI for Radiology, n.d.).
Many disease-specific AI-based algorithms target Alzheimer’s disease (AD), which is pathologically characterized by extracellular plaques composed of β-amyloid and intracellular neurofibrillary tangles containing tau and leads to progressive amnestic and non-amnestic cognitive impairment (Knopman et al., 2021). Some of these algorithms are capable of distinguishing between AD and cognitively normal individuals using MRI with sensitivities ranging from 78-99.1% and specificities ranging from 70- 92.68%(Battineni et al., 2022). An approach based on non-linear support vector machines was able to differentiate between AD and other dementia syndromes like frontotemporal lobar degeneration with an accuracy of 84% (Davatzikos et al., 2008).
Efforts have also been made to predict the conversion from the prodromal phase of AD to clinical AD as it is believed that the former is when therapeutic interventions might be particularly effective (Crous- Bou et al., 2017). Mild cognitive impairment (MCI) describes a condition when individuals have more severe cognitive deficits than is expected for their age but where this does not interfere significantly with their daily activities (Petersen, 2016). Several AI-based approaches have been used to predict conversion from MCI to AD with accuracies of 66-92% (Amoroso et al., 2018; Bron et al., 2015; Lebedev et al., 2014; G. Lee et al., 2019; Lu et al., 2018; Moradi et al., 2015; Ocasio & Duong, 2021; Salvatore et al., 2015; Spasov et al., 2019).
Early diagnosis is also considered important for the effective treatment of Parkinson’s disease (PD) (Pagan, 2012), another com¬mon neurodegenerative disease characterized pathologically by degeneration of dopaminergic neurons in the substantia nigra. By the time the motor symptoms that point towards a clinical diagnosis of PD appear, it is estimated that more than 60% of the brain’s dopaminergic neurons have been lost (GBD 2016 Parkinson’s Disease Collaborators, 2018). Several machine-learning approaches have been developed to distinguish between PD and healthy controls using morphological features derived from structural MRI (Adeli et al., 2016; Chakraborty et al., 2020; Peng et al., 2017), functional MRI (Long et al., 2012; Pläschke et al., 2017; Tang et al., 2017), positron emission tomography (PET) (Piccardo et al., 2021), and single-positron emission computed tomography (SPECT) (Choi et al., 2017; Hirschauer et al., 2015; Ozsahin et al., 2020), often in combination with clinical scores.
Because the motor symptoms of PD overlap with those of other neurological conditions, the clinical features alone are often not sufficient to confidently diagnose PD (Rizzo et al., 2016). Distinguishing idiopathic PD from atypical parkinsonian syndromes such as multisystem atrophy and progressive supranuclear palsy based on clinical features is particularly challenging (Rizzo et al., 2016). Leveraging the potential of neuroimaging to help make this distinction, an early study used support vector machine learning to classify idiopathic PD and other causes of parkinsonism using diffusion tensor imaging with a sensitivity of 94% and specificity of 100% (Haller et al., 2012). Several other studies showed high accuracies for distinguishing between idiopathic PD and atypical parkinsonism using structural MRI (Duchesne et al., 2009; Focke et al., 2011; Huppertz et al., 2016; Marquand et al., 2013; Salvatore et al., 2014), susceptibility-weighted imaging (Haller et al., 2013), and a combination of diffusion tensor imaging and structural MRI (Cherubini et al., 2014).
Studies have also been performed using machine learning models to help guide PD treatment. A study of 67 PD patients found that features extracted from functional MRI can classify optimal vs suboptimal parameters for deep brain stimulation with 88% accuracy (Boutet et al., 2021). This may help optimize the currently lengthy, costly, and cumbersome process of extensive clinical testing required to optimize deep brain stimulation parameters in PD patients.
Multiple sclerosis (MS) is a common autoimmune disorder of the central nervous system characterized pathologically by inflammatory demyelination and leading to a wide range of neurological manifestations (McGinley et al., 2021). MRI plays an important role in the diagnosis and management of MS and is the imaging technique of choice for quantifying and classifying MS lesions in the brain and spinal cord (Matthews et al., 2016). Imaging features are a crucial part of the diagnostic criteria for MS (Thompson et al., 2018) and guidelines recommend that MRI be used to monitor patients and guide treatment (Wattjes et al., 2015). Several AI-based algorithms have received FDA clearance and CE certification for the quantification of brain atrophy and automated segmentation of lesions in MS (Cavedo et al., 2022; Qubiotech Neurocloud Vol, 2021; Zaki et al., 2022).
Many AI-based algorithms in MS focus on the automated extraction of imaging features (Afzal et al., 2022; Bonacchi et al., 2022; Eichinger et al., 2020; Moazami et al., 2021). Visual assessment of the presence of MS lesions and their progression over time is an important part of MS diagnosis and monitoring but is time-consuming and difficult (Danelakis et al., 2018). Instead, several traditional machine learning (Brosch et al., 2016; Goldberg-Zimring et al., 1998; Karimian & Jafari, 2015; Samarasekera et al., 1997; Schmidt et al., 2012; S. Zhang et al., 2018) and deep learning approaches (Birenbaum & Greenspan, 2017; Deshpande et al., 2015; Roy et al., 2018; Valverde et al., 2017, 2019) for automatically segmenting MS lesions have been developed. About 30% of these studies use CNNs and 40% use support vector machine learning approaches (Afzal et al., 2022).
Deep learning approaches have yielded Dice similarity coefficients (a measure of spatial overlap ranging from 0 to 1) of 0.52 to 0.67 compared to manual lesion segmentations (Afzal et al., 2022). Several AI-based approaches to automatically quantify brain atrophy, which is another imaging predictor of MS evolution (Andravizou et al., 2019), have also been investigated (Dolz et al., 2018; Kushibar et al., 2018; Wachinger et al., 2018).
AI-based algorithms have also been leveraged to identify MRI abnormalities that are not clearly visible to the naked eye and are not included in the current diagnostic criteria for MS. These include abnormalities of the cerebral veins and iron deposition detected using susceptibility-weighted imaging (Lopatina et al., 2020) and abnormalities in normal-appearing areas of the white and gray matter in both conventional (Eitel et al., 2019) and advanced MRI sequences (Neeb & Schenk, 2019; Saccà et al., 2019; Yoo et al., 2018; Zurita et al., 2018).
Excluding diseases with a similar clinical presentation is necessary for the diagnosis of MS but is sometimes difficult (Wildner et al., 2020). Using features extracted from MRI, random forests and CNNs have yielded accuracies in distinguishing between MS and neuromyelitis optica spectrum disorders (Eshaghi et al., 2016; Rocca et al., 2021), non-inflammatory disorders of the white matter (Mangeat et al., 2020; Theocharakis et al., 2009), migraine (Rocca et al., 2021), vasculitis of the central nervous system (Rocca et al., 2021), and brain tumors (Ekşi et al., 2021).
MS is divided into several clinical phenotypes that have different prognoses and optimal treatment strategies (Lublin et al., 2014). Using diffusion tensor MRI (Kocevar et al., 2016; Marzullo et al., 2019), magnetic resonance spectroscopy (EkŞİ et al., 2020; Ion-Mărgineanu et al., 2017), and MR-based atrophy measures (Bonacchi et al., 2020), several studies have investigated the potential of AI-based approaches designed to distinguish between different MS clinical phenotypes.
MS treatment is personalized based on clinical, demographic, laboratory, and imaging prognostic markers (Rotstein & Montalban, 2019). Several AI-based algorithms have been evaluated for the ability to predict conversion from the first clinical episode suggestive of a chronic inflammatory CNS disease, known as a “clinically isolated syndrome”, to definite MS using MRI features with sensitivities of 64-77% and specificities of 66-78% (Bendfeldt et al., 2019; Wottschel et al., 2015, 2019). AI-based algorithms combining clinical and MRI data have also been designed to predict disease course and clinical disability (Filippi et al., 2013; Roca et al., 2020; Tommasin et al., 2021; Zhao et al., 2017, 2020). Using support vector machines and extremely randomized trees, a study found that a high-dimensional imaging “fingerprint” derived from T1-weighted images and FLAIRs was better at predicting MS treatment response than measures of treatment response derived from conventional MRI such as brain volume and the number and volume of lesions (AUC 0.89 vs. 0.69) (Kanber et al., 2019).
In addition, AI-based algorithms have shown potential for aiding MRI protocols used in MS. This includes extracting information from conventional MRI sequences, generating synthetic sequences from acquired images, for example, contrast-enhanced images from unenhanced MRI (Bonacchi et al., 2022).
Brain tumors include both primary tumors of the brain (which can be either benign or malignant) and metastatic tumors from elsewhere in the body. Distinguishing between brain tumors and other conditions on imaging is important to avoid unnecessary biopsies and guide management (Abd- Ellah et al., 2019). By extracting features from T1- and T2-weighted MRIs, a regression-based classifier achieved an AUC of up to 0.99 for distinguishing between non-enhancing gliomas and inflammatory brain lesions (Y. Han et al., 2021). AI-based approaches have also been designed to predict glioma histopathological grades based on MRI, achieving an average accuracy of 89% ± 0.09%, reported in a systematic literature review (Bahar et al., 2022). Additionally, a random forest classifier using features from multiparametric MRI performed better than two senior radiologists at identifying the primary tumor in cases of brain metastasis (Kniep et al., 2019).
One of the most promising use cases for AI in neurooncological imaging is for using MRI to identify genetic mutations associated with tumors. This field, known as radiogenomics, is important because histopathologically similar tumors with different mutations respond to specific treatment strategies differently (Singh et al., 2021). Radiogenomics involves extracting features from multiple MRI sequences (anatomical sequences as well as diffusion-weighted and perfusion sequences) and using these features to predict genomic alterations in the tumor. Several approaches using decision trees and random forests have been designed to predict both single genetic mutations as well as more complex sets of genomic alterations in brain tumors (Akkus et al., 2017; P. Chang et al., 2018; L. Han & Kamdar, 2018; Hu et al., 2017; Kickingereder et al., 2016; Park et al., 2020). Imaging also plays an important role in assessing brain tumor response to treatments such as radiation, immunotherapy, chemotherapy, and surgery. This requires significant expertise, especially because imaging features of response and recurrence overlap with other treatment-related changes such as pseudoprogression, which is a transient increase in contrast enhancement and/or peritumoral edema after radiotherapy and chemotherapy (Raimbault et al., 2014; Thust et al., 2018).
In glioblastoma, the most common malignant primary brain tumor in adults, treatment response assessment involves manually measuring the volume of tumor tissue that takes up contrast agent (Leao et al., 2020). Accurate automated segmentation of brain tumor tissue has been achieved using support vector machine learning, random forests, and CNNs (Havaei et al., 2017; Kickingereder et al., 2019; Menze et al., 2015), with some CE-certified applications available (BioMind, n.d.). A meta-analysis found that deep learning outperforms traditional machine learning approaches for tumor segmentation (Kouli et al., 2022). In a large, multicenter study on a clinical data, automated tumor volumetry using CNNs showed suggested superiority to manual volumetry in calculating time to disease progression (Kickingereder et al., 2019).
Approaches using regression-based classification and CNNs have shown results for distinguishing between pseudoprogression and true progression of brain tumors on MRI (Jang et al., 2018, 2020; J. Y. Kim et al., 2019).
CNNs have also shown promise for differentiating radiation necrosis from tumor progression, achieving a sensitivity of 99.4% and specificity of 97.5% (Q. Zhang et al., 2019). The margins of brain tumors and their infiltration of surrounding tissue can be very difficult to distinguish from peritumoral edema. Regression-based classifiers, support vector machine learning classifiers, and CNSs have yielded accurate maps of peritumoral infiltration that may prove useful for surgical planning (Akbari et al., 2016; P. D. Chang, Chow, et al., 2017; P. D. Chang, Malone, et al., 2017).
There has been interest in AI-based approaches designed to improve MRI and assist radiologists with the diagnosis of brain tumors. Some targets for these AI-based approaches include, Image reconstruction algorithms based on CNNs improve spatial resolution and reduce noise, allowing smaller anatomical structures and tumor components to be seen. Such approaches have shown results for detecting pituitary microadenomas, identifying residual or recurrent tumor after treatment, and characterizing tumor invasion (M. Kim et al., 2021; D. H. Lee et al., 2021). Moreover, contrast-enhanced images synthesized from contrast-free images using generative adversarial networks (GANs) have been found to be useful for assessing response to glioblastoma treatment and may help reduce the use of MRI contrast agents (Jayachandran Preetha et al., 2021).
In the span of about a decade, research into applications of AI in neuroradiology has made remarkable progress. AI has been particularly useful in supporting the diagnosis of conditions such as stroke and intracranial hemorrhage, where timely detection is crucial. There is also growing evidence that AI could be used to monitor the progression of neurological conditions, predict outcomes, and ultimately allow for more personalized and effective treatment strategies. Research on AI-based algorithms should be supplemented in the future by analyzing the cost-effectiveness of these applications and the measuring the effect of their implementation on overall patient outcomes. In addition, these applications should be backed by more published data on their performance to encourage their use. Overall, the use of AI in neuroradiology holds great promise for improving the quality of patient care.
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