The Search Patterns of Abdominal Imaging Subspecialists for Abdominal Computed Tomography: Toward a Foundational Pattern for New Radiology Residents

Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality Guided Robotic Partial Nephrectomy

OBJECTIVES

The research's purpose is to develop a software that automatically integrates and overlay 3D virtual models of kidneys harboring renal masses into the Da Vinci robotic console, assisting surgeon during the intervention.

INTRODUCTION

Precision medicine, especially in the field of minimally-invasive partial nephrectomy, aims to use 3D virtual models as a guidance for augmented reality robotic procedures. However, the co-registration process of the virtual images over the real operative field is performed manually.

METHODS

In this prospective study, two strategies for the automatic overlapping of the model over the real kidney were explored: the computer vision technology, leveraging the super-enhancement of the kidney allowed by the intraoperative injection of Indocyanine green for superimposition and the convolutional neural network technology, based on the processing of live images from the endoscope, after a training of the software on frames from prerecorded videos of the same surgery. The work-team, comprising a bioengineer, a software-developer and a surgeon, collaborated to create hyper-accuracy 3D models for automatic 3D-AR-guided RAPN. For each patient, demographic and clinical data were collected.

RESULTS

Two groups (group A for the first technology with 12 patients and group B for the second technology with 8 patients) were defined. They showed comparable preoperative and post-operative characteristics. Concerning the first technology the average co-registration time was 7 (3-11) seconds while in the case of the second technology 11 (6-13) seconds. No major intraoperative or postoperative complications were recorded. There were no differences in terms of functional outcomes between the groups at every time-point considered.

CONCLUSION

The first technology allowed a successful anchoring of the 3D model to the kidney, despite minimal manual refinements. The second technology improved kidney automatic detection without relying on indocyanine injection, resulting in better organ boundaries identification during tests. Further studies are needed to confirm this preliminary evidence.

When Not to Operate on Acute Cases-A Surgeon's Perspective on Rapid Assessment of Emergency Abdominopelvic Computed Tomography

Clinical problem solving evolves in parallel with advances in technology and discoveries in the medical field. However, it always reverts to basic cognitive processes involved in critical thinking, such as hypothetical-deductive reasoning, pattern recognition, and compilation models. When dealing with cases of acute abdominal pain, clinicians should employ all available tools that allow them to rapidly refine their analysis for a definitive diagnosis. Therefore, we propose a standardized method for the quick assessment of abdominopelvic computed tomography as a supplement to the traditional clinical reasoning process. This narrative review explores the cognitive basis of errors in reading imaging. It explains the practical use of attenuation values, contrast phases, and windowing for non-radiologists and details a multistep protocol for finding radiological cues during CT reading and interpretation. This systematic approach describes the salient features and technical tools needed to ascertain the causality between clinical patterns and abdominopelvic changes visible on CT scans from a surgeon's perspective. It comprises 16 sections that should be read successively and that cover the entire abdominopelvic region. Each section details specific radiological signs and provides clear explanations for targeted searches, as well as anatomical and technical hints. Reliance on imaging in clinical problem solving does not make a decision dichotomous nor does it guarantee success in diagnostic endeavors. However, it contributes exact information for supporting the clinical assessments even in the most subtle and intricate conditions.

Diagnostic Value of Combined Detection of Pelvic Ultrasound and Serum LH, FSH, and E2 Levels in Children with Idiopathic Central Precocious Puberty

Objective

To study the diagnostic value of combined detection of pelvic ultrasound and serum LH, FSH, and E2 levels in children with idiopathic central precocious puberty (ICPP).

Methods

30 cases of children with ICPP admitted to our hospital from January 2019 to January 2021 were selected as the experimental group, and 30 healthy people during the same period were selected as the control group. Both groups received pelvic ultrasound and serum LH, FSH, and E2 detection; the two groups were compared in terms of serum indicators, combined diagnosis, specificity, and sensitivity.

Results

There were statistical differences in height, leptin, bone age, and areola diameter between the two groups (p < 0.05). The length of the uterus, the volume of the uterus, the area of the ovary, the volume of the ovary, and the maximum diameter of the follicle in the experimental group were larger than those in the control group (p < 0.05). The endometrial thickness of the experimental group was significantly greater than that of the control group (p < 0.05). The levels of serum LH, FSH, and E2 in the experimental group were significantly higher than those in the control group (p < 0.05). The area of the combined detection was significantly larger than that of the single detection. The combined detection was superior to the single detection with respect to the area, standard error a, asymptotic Sig. B, and asymptotic 95% confidence interval (p < 0.05). The sensitivity of the combined detection was significantly higher than that of the single detection.

Conclusion

The combined detection of pelvic ultrasound and serum LH, FSH, and E2 levels may be a preferred technique for the diagnosis of children with ICPP due to its benefits of high sensitivity and accuracy. It is worthy of clinical promotion and application.

Mandating Limits on Workload, Duty, and Speed in Radiology

Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.

How to Read an Abdominal CT: Insights from the Visual and Cognitive Sciences Translated for Clinical Practice

When first learning abdominal CT studies, residents are often given little concrete, practical direction. There is, however, a large literature from the visual and cognitive sciences that can provide guidance towards search strategies that maximize efficiency and comprehensiveness. This literature has not penetrated radiology teaching to any great extent. In this article, we will examine the current pedagogy (and why that falls short), why untutored search fails, where misses occur in abdomen/pelvis CT, why these misses occur where they do, how expert radiologists search 3d image stacks, and how novices might expedite the acquisition of expertise.