Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.

Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.

A Longitudinal Analysis of Early Lesion Growth in Presymptomatic Patients with Cerebral Adrenoleukodystrophy.

BACKGROUND AND PURPOSE: Cerebral adrenoleukodystrophy is a devastating neurological disorder caused by mutations in the ABCD1 gene. Our aim was to model and compare the growth of early cerebral lesions from longitudinal MRIs obtained in presymptomatic patients with progressive and arrested cerebral adrenoleukodystrophy using quantitative MR imaging-based lesion volumetry. MATERIALS AND METHODS: We retrospectively quantified and modeled the longitudinal growth of early cerebral lesions from 174 MRIs obtained from 36 presymptomatic male patients with cerebral adrenoleukodystrophy. Lesions were manually segmented using subject-specific lesion-intensity thresholding. Volumes were calculated and plotted across time. Lesion velocity and acceleration were calculated between sequentially paired and triplet MRIs, respectively. Linear mixed-effects models were used to assess differences in growth parameters between progressive and arrested phenotypes. RESULTS: The median patient age was 7.4 years (range, 3.9-37.0 years). Early-stage cerebral disease progression was inversely correlated with age (ρ = -0.6631, P < .001), early lesions can grow while appearing radiographically stable, lesions undergo sustained acceleration in progressive cerebral adrenoleukodystrophy (ß = 0.10 mL/month2 [95% CI, 0.05-0.14 mL/month2], P < .001), and growth trajectories diverge between phenotypes in the presymptomatic time period. CONCLUSIONS: Measuring the volumetric changes in newly developing cerebral lesions across time can distinguish cerebral adrenoleukodystrophy phenotypes before symptom onset. When factored into the overall clinical presentation of a patient with a new brain lesion, quantitative MR imaging-based lesion volumetry may aid in the accurate prediction of patients eligible for therapy.

Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time: a 3T diffusion tensor imaging study of mild traumatic brain injury.

BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) may be a useful index of microstructural changes implicated in diffuse axonal injury (DAI) linked to persistent postconcussive symptoms, especially in mild traumatic brain injury (TBI), for which conventional MR imaging techniques may lack sensitivity. We hypothesized that for mild TBI, DTI measures of DAI would correlate with impairments in reaction time, whereas the number of focal lesions on conventional 3T MR imaging would not. MATERIALS AND METHODS: Thirty-four adult patients with mild TBI with persistent symptoms were assessed for DAI by quantifying traumatic microhemorrhages detected on a conventional set of T2*-weighted gradient-echo images and by DTI measures of fractional anisotropy (FA) within a set of a priori regions of interest. FA values 2.5 SDs below the region average, based on a group of 26 healthy control adults, were coded as exhibiting DAI. RESULTS: DTI measures revealed several predominant regions of damage including the anterior corona radiata (41% of the patients), uncinate fasciculus (29%), genu of the corpus callosum (21%), inferior longitudinal fasciculus (21%), and cingulum bundle (18%). The number of damaged white matter structures as quantified by DTI was significantly correlated with mean reaction time on a simple cognitive task (r = 0.49, P = .012). In contradistinction, the number of traumatic microhemorrhages was uncorrelated with reaction time (r = -0.08, P = .71). CONCLUSION: Microstructural white matter lesions detected by DTI correlate with persistent cognitive deficits in mild TBI, even in populations in which conventional measures do not. DTI measures may thus contribute additional diagnostic information related to DAI.