Approximately 10% of all significant blunt abdominal traumatic injuries manifest with renal injury, although it is usually minor. However, renal imaging is indicated in cases of gross hematuria, penetrating trauma with gross or microscopic hematuria, and blunt trauma and shock with gross or microscopic hematuria. Contrast material-enhanced computed tomography (CT) is the imaging modality of choice in the evaluation and management of renal trauma. Contrast-enhanced CT is readily available in emergency departments and can quickly and accurately depict renal injuries as well as associated injuries to other abdominal or retroperitoneal organs. In this way, contrast-enhanced CT provides the anatomic and functional information that is essential for accurate staging. In addition, CT can help detect active hemorrhage and urinary extravasation and is very useful in guiding transcatheter embolization and delineating preexisting disease entities that may predispose kidneys to posttraumatic hemorrhage. With the advent of multidetector CT, imaging is characterized by faster scanning times, increased volume coverage, and improved spatial and temporal resolution. The increased use of CT has been partially responsible for a growing trend toward conservative management of renal trauma, except in cases in which extensive urinary extravasation or devitalized areas of renal parenchyma are found and especially in those cases with associated injuries to other abdominal organs; these cases are particularly prone to complications and usually require surgery.

Cerebrospinal fluid (CSF) fistulas are characterized by the egress of CSF from the intracranial cavity through an osteodural disruption between the subarachnoid space and a pneumatized structure within the skull base. Depending on the cause, CSF fistulas are classified as acquired or congenital, and acquired fistulas are further classified as traumatic, nontraumatic, or spontaneous. Spontaneous CSF fistulas are considered to result from a multifactorial process and have been postulated to represent a variant of idiopathic intracranial hypertension. However, an anatomic predisposition involving thinning of the cranial base, such as pneumatization of the sinus walls, must also be present. This process creates areas of structural weakness that act as potential pathways for CSF leaks, which most commonly occur in the ethmoid roof, sphenoid sinus, and temporal bone. Because CSF leaks may be overlooked, a result of their asymptomatic or subtle, intermittent course, a high level of suspicion is crucial in making an early diagnosis. However, CSF fistulas may be well seen at computed tomography (CT), which depicts bone defects, and magnetic resonance cisternography, which reveals the contents of herniated tissue. Knowledge of the location and size of the bone defect and herniated contents is crucial for the selection of surgical approach and grafting material.

Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice.

Background/Objectives: The growing use of artificial intelligence (AI) in musculoskeletal radiographs presents significant potential to improve diagnostic accuracy and optimize clinical workflow. However, assessing its performance in clinical environments is essential for successful implementation. We hypothesized that our AI applied to urgent bone X-rays could detect fractures, joint dislocations, and effusion with high sensitivity (Sens) and specificity (Spec). The specific objectives of our study were as follows: 1. To determine the Sens and Spec rates of AI in detecting bone fractures, dislocations, and elbow joint effusion compared to the gold standard (GS). 2. To evaluate the concordance rate between AI and radiology residents (RR). 3. To compare the proportion of doubtful results identified by AI and the RR, and the rates confirmed by GS. Methods: We conducted an observational, double-blind, retrospective study on adult bone X-rays (BXRs) referred from the emergency department at our center between October and November 2022, with a final sample of 792 BXRs, categorized into three groups: large joints, small joints, and long-flat bones. Our AI system detects fractures, dislocations, and elbow effusions, providing results as positive, negative, or doubtful. We compared the diagnostic performance of AI and the RR against a senior radiologist (GS). Results: The study population’s median age was 48 years; 48.6% were male. Statistical analysis showed Sens = 90.6% and Spec = 98% for fracture detection by the RR, and 95.8% and 97.6% by AI. The RR achieved higher Sens (77.8%) and Spec (100%) for dislocation detection compared to AI. The Kappa coefficient between RR and AI was 0.797 for fractures in large joints, and concordance was considered acceptable for all other variables. We also analyzed doubtful cases and their confirmation by GS. Additionally, we analyzed findings not detected by AI, such as chronic fractures, arthropathy, focal lesions, and anatomical variants. Conclusions: This study assessed the impact of AI in a real-world clinical setting, comparing its performance with that of radiologists (both in training and senior). AI achieved high Sens, Spec, and AUC in bone fracture detection and showed strong concordance with the RR. In conclusion, AI has the potential to be a valuable screening tool, helping reduce missed diagnoses in clinical practice.