Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them.

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow. This paper explores AI's impact on neuroradiology, an area accounting for a substantial portion of recent radiology studies. We present a case series evaluating an AI software's performance in detecting neurovascular findings, highlighting five cases where AI interpretations differed from radiologists' assessments. Our study underscores common pitfalls of AI in the context of CT head angiograms, aiming to guide future AI algorithms.  Methods We conducted a retrospective case series study at Stony Brook University Hospital, a large medical center in Stony Brook, New York, spanning from October 1, 2021 to December 31, 2021, analyzing 140 randomly sampled CT angiograms using AI software. This software assessed various neurovascular parameters, and AI findings were compared with neuroradiologists' interpretations. Five cases with divergent interpretations were selected for detailed analysis. Results Five representative cases in which AI findings were discordant with radiologists' interpretations are presented with diagnoses including diffuse anoxic ischemic injury, cortical laminar necrosis, colloid cyst, right superficial temporal artery-to-middle cerebral artery (STA-MCA) bypass, and subacute bilateral subdural hematomas. Discussion The errors identified in our case series expose AI's limitations in radiology. Our case series reveals that AI's incorrect interpretations can stem from complexities in pathology, challenges in distinguishing densities, inability to identify artifacts, identifying post-surgical changes in normal anatomy, sensitivity limitations, and insufficient pattern recognition. AI's potential for improvement lies in refining its algorithms to effectively recognize and differentiate pathologies. Incorporating more diverse training datasets, multimodal data, deep-reinforcement learning, clinical context, and real-time learning capabilities are some ways to improve AI's performance in the field of radiology. Conclusion Overall, it is apparent that AI applications in radiology have much room for improvement before becoming more widely integrated into clinical workflows. While AI demonstrates remarkable potential to aid in diagnosis and streamline workflows, our case series highlights common pitfalls that underscore the need for continuous improvement. By refining algorithms, incorporating diverse datasets, embracing multimodal information, and leveraging innovative machine learning strategies, AI's diagnostic accuracy can be significantly improved.

A Review of COVID-19's Impact on Radiology Residents' Academics and Wellness in North America.

BACKGROUND: The COVID-19 pandemic caused major disruptions in radiology departments throughout North America. Radiology residency programs were forced to make dramatic changes to their training programs, which had major impacts on resident academics and wellness. The goal of this review is to evaluate the impact of COVID-19 on radiology residents' academics and wellness in North America, while also identifying effective measures taken by programs to mitigate the effects of the pandemic. METHODS: The search strategy involved database search via PubMed, Embase, and Web of Science with specific key words related to COVID-19, radiology residents, education, wellness, and virtual learning. Studies discussing the education and wellness of radiology residents in North America published after 2020 were included. The data were analyzed using a narrative synthesis approach. RESULTS: The three main domains affected by the pandemic include the residency curriculum, research, and resident wellness. The decline in case volume and diversity of cases had negative overall impact on education of radiology residents, but simulated cases and virtual learning proved its value during the pandemic and may have lasting implications for the postpandemic world. Research initiatives transitioned to a remote format with greater emphasis on quality improvement and COVID-19-related studies. Reduced face-to-face interaction opportunities made it difficult to establish strong and meaningful interpersonal connections and had a negative impact on resident wellness, mentorship, and professional development. Implementing mentorship programs and virtual "town hall meetings" were effective measures to maintain connections during times of social distancing. Finally, the COVID-19 pandemic introduced unprecedented stressors and challenges for radiology residents that negatively impacted their mental health and wellness. Incorporating wellness initiatives such as wellness hours and team-building activities and using social media were helpful in promoting wellness and mental health for radiology residents. CONCLUSION: The COVID-19 pandemic has had a significant impact on the academics and wellness of radiology residents across North America but has taught us many lessons that can help us navigate the ongoing challenges of the pandemic, the postpandemic world, and future pandemics.

Call to Action: Creating Resources for Radiology Technologists to Capture Higher Quality Portable Chest X-rays.

Background Patient rotation, foreign body overlying anatomy, and anatomy out of field of view can have detrimental impacts on the diagnostic quality of portable chest x-rays (PCXRs), especially as the number of PCXR imaging increases due to the coronavirus disease 2019 (COVID-19) pandemic. Although preventable, these "quality failures" are common and may lead to interpretative and diagnostic errors for the radiologist. Aims In this study, we present a baseline quality failure rate of PCXR imaging as observed at our institution. We also conduct a focus group highlighting the key issues that lead to the problematic images and discuss potential interventions targeting technologists that can be implemented to address imaging quality failure rate. Materials and methods A total of 500 PCXRs for adult patients admitted to a large university hospital between July 12, 2021, and July 25, 2021, were obtained for evaluation of quality. The PCXRs were evaluated by radiology residents for failures in technical image quality. The images were categorized into various metrics including the degree of rotation and obstruction of anatomical structures. After collecting the data, a focus group involving six managers of the technologist department at our university hospital was conducted to further illuminate the key barriers to quality PCXRs faced at our institution.. Results  Out of the 500 PCXRs evaluated, 231 were problematic (46.2%). 43.5% of the problematic films with a repeat PCXR within one week showed that there was a technical problem impacting the ability to detect pathology. Most problematic films also occurred during the night shift (48%). Key issues that lead to poor image quality included improper patient positioning, foreign objects covering anatomy, and variances in technologists' training. Three interventions were proposed to optimize technologist performance that can lower quality failure rates of PCXRs. These include a longitudinal educational curriculum involving didactic sessions, adding nursing support to assist technologists, and adding an extra layer of verification by internal medicine residents before sending the films to the radiologist. The rationale for these interventions is discussed in detail so that a modified version can be implemented in other hospital systems.  Conclusion This study illustrates the high baseline error rate in image quality of PCXRs at our institution and demonstrates the need to improve on image quality. Poor image quality negatively impacts the interpretive accuracy of radiologists and therefore leads to wrong diagnoses. Increasing educational resources and support for technologists can lead to higher image quality and radiologist accuracy.

Silver nanoparticles alter learning and memory formation in an aquatic organism, Lymnaea stagnalis.

We tested the effect of silver nanoparticles (AgNPs) on the ability of the pond snail, Lymnaea stagnalis, to learn and form long-term memory (LTM) following operant conditioning of aerial respiration. We hypothesized that the AgNPs would act as a stressor and prevent learning and LTM formation. We tested snails exposed for either 72 h or only during training and testing for memory (i.e. 0.5 h) and found no difference between those treatments. We found that at a low concentration of AgNPs (5 µg/L) neither learning and nor memory formation were altered. When we increased the concentration of AgNPs (10 µg/L) we found that memory formation was enhanced. Finally, at a higher concentration (50 µg/L) memory formation was blocked. To determine if the disassociation of Ag+ from the AgNPs caused the effects on memory we performed similar experiments with AgNO3 and found similar concentration-dependent results. Finally, we found that snails perceive the AgNPs differently from Ag+ as there was context specific memory. That is, snails trained in AgNPs did not show memory when tested in Ag+ and vice-versa. We believe that changes in memory formation may be a more sensitive determination of AgNPs on aquatic organisms than the determination of a LC50.

Use of Computer Vision Tools to Identify Behavioral Markers of Pediatric Obsessive-Compulsive Disorder: A Pilot Study.

OBJECTIVES: The clinical presentation of pediatric obsessive-compulsive disorder (OCD) is heterogeneous, which is a stumbling block to understanding pathophysiology and to developing new treatments. A major shift in psychiatry, embodied in the Research Domain Criteria (RDoC) initiative of National Institute of Mental Health, recognizes the pitfalls of categorizing mental illnesses using diagnostic criteria. Instead, RDoC encourages researchers to use a dimensional approach, focusing on narrower domains of psychopathology to characterize brain-behavior relationships. Our aim in this multidisciplinary pilot study was to use computer vision tools to record OCD behaviors and to cross-validate these behavioral markers with standard clinical measures. METHODS: Eighteen youths with OCD and 21 healthy controls completed tasks in an innovation laboratory (free arrangement of objects, hand washing, arrangement of objects on contrasting carpets). Tasks were video-recorded. Videos were coded by blind raters for OCD-related behaviors. Children's Yale-Brown Obsessive Compulsive Scale (CY-BOCS) and other scales were administered. We compared video-recorded measures of behavior in OCD versus healthy controls and correlated video measures and clinical measures of OCD. RESULTS: Behavioral measures on the videos were significantly correlated with specific CY-BOCS dimension scores. During the free arrangement task, more time spent ordering objects and more moves of objects were both significantly associated with higher CY-BOCS ordering/repeating dimension scores. Longer duration of hand washing was significantly correlated with higher scores on CY-BOCS ordering/repeating and forbidden thoughts dimensions. During arrangement of objects on contrasting carpets, more moves and more adjustment of objects were significantly associated with higher CY-BOCS ordering/repeating dimension scores. CONCLUSION: Preliminary data suggest that measurement of behavior using video recording is a valid approach for quantifying OCD psychopathology. This methodology could serve as a new tool for investigating OCD using an RDoC approach. This objective, novel behavioral measurement technique may benefit both researchers and clinicians in assessing pediatric OCD and in identifying new behavioral markers of OCD. Clinical Trial Registry: Development of an Instrument That Monitors Behaviors Associated With OCD. NCT02866422. http://clinicaltrials.gov.