AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats.

Introduction: Artificial intelligence and data-driven predictive modeling have become increasingly common tools integrated in clinical practice, heralding a new chapter of medicine in the digital era. While these techniques are poised to affect nearly all aspects of medicine, medical education as an institution has languished behind; this has raised concerns that the current training infrastructure is not adequately preparing future physicians for this changing clinical landscape. Our institution attempted to ameliorate this by implementing a novel artificial intelligence in radiology curriculum, "AI-RADS," in two different educational formats: a 7-month lecture series and a one-day workshop intensive. Methods: The curriculum was structured around foundational algorithms within artificial intelligence. As most residents have little computer science training, algorithms were initially presented as a series of simple observations around a relatable problem (e.g., fraud detection, movie recommendations, etc.). These observations were later re-framed to illustrate how a machine could apply the underlying concepts to perform clinically relevant tasks in the practice of radiology. Secondary lessons in basic computing, such as data representation/abstraction, were integrated as well. The lessons were ordered such that these algorithms were logical extensions of each other. The 7-month curriculum consisted of seven lectures paired with seven journal clubs, resulting in an AI-focused session every two weeks. The workshop consisted of six hours of content modified for the condensed format, with a final integrative activity. Results: Both formats of the AI-RADS curriculum were well received by learners, with the 7-month version and workshop garnering 9.8/10 and 4.3/5 ratings, respectively, for overall satisfaction. In both, there were increases in perceived understanding of artificial intelligence. In the 7-lecture course, 6/7 lectures achieved statistically significant (P < 0.02) differences, with the final lecture approaching significance (P = 0.07). In the one-day workshop, there was a significant increase in perceived understanding (P = 0.03). Conclusion: As artificial intelligence becomes further enmeshed in clinical practice, it will become critical for physicians to have a basic understanding of how these tools work. Our AI-RADS curriculum demonstrates that it is successful in increasing learner perceived understanding in both an extended and condensed format.

Detection of subclinical hemorrhage using electrical impedance: a porcine study.

Objective.Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.Approach. Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as <15% decrease in BP and <20% increase in HR-i.e.hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage-ideally as early as possible.Main results. Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (∼15.4 min) using an EIT-data-based metric and 0.79 at a 120 ml bleed (∼23.1 min) from invasively measured BP-i.e.the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions.Significance.In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.

AI-RADS: An Artificial Intelligence Curriculum for Residents.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) has rapidly emerged as a field poised to affect nearly every aspect of medicine, especially radiology. A PubMed search for the terms "artificial intelligence radiology" demonstrates an exponential increase in publications on this topic in recent years. Despite these impending changes, medical education designed for future radiologists have only recently begun. We present our institution's efforts to address this problem as a model for a successful introductory curriculum into artificial intelligence in radiology titled AI-RADS. MATERIALS AND METHODS: The course was based on a sequence of foundational algorithms in AI; these algorithms were presented as logical extensions of each other and were introduced as familiar examples (spam filters, movie recommendations, etc.). Since most trainees enter residency without computational backgrounds, secondary lessons, such as pixel mathematics, were integrated in this progression. Didactic sessions were reinforced with a concurrent journal club highlighting the algorithm discussed in the previous lecture. To circumvent often intimidating technical descriptions, study guides for these papers were produced. Questionnaires were administered before and after each lecture to assess confidence in the material. Surveys were also submitted at each journal club assessing learner preparedness and appropriateness of the article. RESULTS: The course received a 9.8/10 rating from residents for overall satisfaction. With the exception of the final lecture, there were significant increases in learner confidence in reading journal articles on AI after each lecture. Residents demonstrated significant increases in perceived understanding of foundational concepts in artificial intelligence across all mastery questions for every lecture. CONCLUSION: The success of our institution's pilot AI-RADS course demonstrates a workable model of including AI in resident education.

Epinephrine plus chest compressions is superior to epinephrine alone in a hypoxia-induced porcine model of pseudo-pulseless electrical activity.

AIM: Pseudo-pulseless electrical activity (pseudo-PEA) is a global hypotensive ischemic state with retained coordinated myocardial contractile activity and an organized ECG with no clinically detectable pulses. The role of standard external chest compressions (CPR) and its associated intrinsic hemodynamics remains unclear in the setting of pseudo-PEA. We undertook an experimental trial to compare epinephrine alone versus epinephrine with CPR in the treatment of pseudo-PEA. METHODS: Using a porcine model of hypoxic pseudo-PEA, we randomized 12 Yorkshire male swine to resuscitation with epinephrine only (control) (0.0015 mg/kg) versus epinephrine plus standard CPR (intervention). Animals who achieved return of spontaneous circulation (ROSC) were stabilized, fully recovered to hemodynamic and respiratory baseline, and rearrested up to 6 times. Primary outcome was ROSC defined as a sustained systolic blood pressure (SBP) of 60 mmHg for 2 min. Secondary outcomes included time to ROSC, coronary perfusion pressure (CoPP), and end-tidal carbon dioxide (ETCO2). RESULTS: Among 47 events of pseudo-PEA in 12 animals, we observed significantly higher proportion of ROSC when treatment included CPR (14/21 - 67%) compared to epinephrine alone (4/26 - 15%) (p = 0.0007). CoPP, aortic pressures and ETCO2 were significantly higher, and right atrial pressures were lower in the intervention group. CONCLUSIONS: In a swine model of hypoxia-induced pseudo-PEA, epinephrine plus CPR was associated with improved intra-arrest hemodynamics and higher probability of ROSC. Thus, epinephrine plus CPR may be superior to epinephrine alone in the treatment of patients with pseudo-PEA.

Intravenous calcium as a pressor in a swine model of hypoxic pseudo-pulseless electrical mechanical activity-a preliminary report.

BACKGROUND: Pseudo-pulseless electrical activity (pseudo-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. Pseudo-PEA may constitute up to 40% of reported cases of cardiac arrest. Resuscitation from pseudo-PEA is often associated with hypotension refractory to catecholamine pressors. We hypothesized that this post-resuscitation state may be associated with hypocalcemic hypotension responsive to intravenous calcium. METHODS: Using pre-existing data from our hypoxic swine pseudo-PEA model, we measured blood pressure, hemodynamics, and electrolytes. Physiological data were analyzed on a heartbeat by heartbeat basis. The midpoint of the calcium response was defined using change of curvature feature detection. Hemodynamic parameters were shifted such that the value at the midpoint was equal to zero. RESULTS: In 9 animals with refractory hypotension, we administered 37 boluses of intravenous calcium in the dosage range of 5-20 mg. Comparisons were made between the average values in the time period 40-37 s before the midpoint and 35-40 s after the midpoint. Of the 37 administered boluses, 34 manifested a change in the blood pressure, with mean aortic pressure, systolic and diastolic pressures all increasing post bolus administration. CONCLUSIONS: Administration of intravenous calcium may be associated with a pressor-like response in refractory hypotension after resuscitation from pseudo-PEA. Relative ionized hypocalcemia may cause hypotension after resuscitation from pseudo-PEA. Therapy with intravenous calcium should be further investigated in this setting.