An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype.

PURPOSE: The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance. METHODS: Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance. To evaluate user acceptance, three versions of the SpleenPro user interface with increasing explainability were presented to four independent expert panelists with four example cases each. A structured interview consisting of Likert scales and free responses was conducted, with specific questions regarding dimensions of diagnostic utility (DU); mental support (MS); effort, workload, and frustration (EWF); trust and reliability (TR); and likelihood of future use (LFU). RESULTS: SpleenPro significantly decreased interpretation times for both raters. Weighted Cohen's kappa increased from 0.53 to 0.70 with AI/ML assistance. During user acceptance interviews, increasing explainability was associated with improvement in Likert scores for MS, EWF, TR, and LFU. Expert panelists indicated the need for a combined early notification and grading functionality, PACS integration, and report autopopulation to improve DU. CONCLUSIONS: SpleenPro was useful for improving objectivity of AAST grading and increasing mental support. Formative user research identified generalizable concepts including the need for a combined detection and grading pipeline and integration with the clinical workflow.

Magnetic resonance imaging spectrum of intracranial tubercular lesions: one disease, many faces.

Tuberculosis is a devastating disease and has shown resurgence in recent years with the advent of acquired immunodeficiency syndrome. Central nervous system involvement is the most devastating form of the disease, comprising 10% of all tuberculosis cases. The causative organism, Mycobacterium tuberculosis, incites a granulomatous inflammatory response in the brain, the effects of which can be appreciated on magnetic resonance imaging (MRI), which can thus be used for diagnosis of the same. Neurotuberculosis can present in various patterns, which can be identified on MRI. The meningeal forms include leptomeningitis and pachymeningitis. Parenchymal forms of neurotuberculosis include tuberculoma in its various stages, tubercular cerebritis and abscess, tubercular rhombencephalitis, and tubercular encephalopathy. Each pattern has characteristic MRI appearances and differential diagnoses on imaging. Complications of neurotuberculosis, usually of tubercular meningitis, include hydrocephalus, vasculitis, and infarcts as well as cranial nerve palsies. Various MRI sequences besides the conventional ones can provide additional insight into the disease, help in quantifying the disease load, and help in differentiation of neurotuberculosis from conditions with similar imaging appearances and presentations. These can enable accurate and timely diagnosis by the radiologist and early institution of treatment in order to reduce the likelihood of permanent neurological sequelae.