Survey of CT radiation doses and iodinated contrast medium administration: an international multicentric study

ObjectiveTo assess the relationship between intravenous iodinated contrast media (ICM) administration usage and radiation doses for contrast-enhanced (CE) CT of head, chest, and abdomen-pelvis (AP) in international, multicenter settings. MethodsOur international (n = 16 countries), multicenter (n = 43 sites), and cross-sectional (ConRad) study had two parts. Part 1: Redcap survey with questions on information related to CT and ICM manufacturer/brand and respective protocols. Part 2: Information on 3,258 patients (18-96 years; M:F 1654:1604) who underwent CECT for a routine head (n = 456), chest (n = 528), AP (n = 599), head CT angiography (n = 539), pulmonary embolism (n = 599), and liver CT examinations (n = 537) at 43 sites across five continents. The following information was recorded: hospital name, patient age, gender, body mass index [BMI], clinical indications, scan parameters (number of scan phases, kV), IV-contrast information (concentration, volume, flow rate, and delay), and dose indices (CTDIvol and DLP). ResultsMost routine chest (58.4%) and AP (68.7%) CECT exams were performed with 2-4 scan phases with fixed scan delay (chest 71.4%; AP 79.8%, liver CECT 50.7%) following ICM administration. Most sites did not change kV across different patients and scan phases; most CECT protocols were performed at 120-140 kV (83%, 1979/2685). There were no significant differences between radiation doses for non-contrast (CTDIvol 24 [16-30] mGy; DLP 633 [414-702] mGycm) and post-contrast phases (22 [19-27] mGy; 648 [392-694] mGycm) (p = 0.142). Sites that used bolus tracking for chest and AP CECT had lower CTDIvol than sites with fixed scan delays (p < 0.001). There was no correlation between BMI and CTDIvol (r2 <= - 0.1 to 0.1, p = 0.931). ConclusionOur study demonstrates up to ten-fold variability in ICM injection protocols and radiation doses across different CT protocols. The study emphasizes the need for optimizing CT scanning and contrast protocols to reduce unnecessary contrast and radiation exposure to patients. Clinical relevance statementThe wide variability and lack of standardization of ICM media and radiation doses in CT protocols suggest the need for education and optimization of contrast usage and scan factors for optimizing image quality in CECT

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi-institutional radiology reports search database of 13 million reports to identify all CXR reports with addendums from 1999–2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addenda were created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports contained addenda (279 patients) with errors related to side-discrepancies or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of the AI in detecting missed or mislabeled findings. Results: The AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for the AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). Conclusions: The CXR AI could accurately detect mislabeled and missed findings. Clinical Relevance: The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings

On the occasion of world kidney day 2023; renal impacts of COVID-19

World kidney day is an international campaign focused on bringing awareness to kidney health throughout the world and reducing the incidence of renal disease and its related medical complications. This mini-review sought to take a short look on the renal impact of SARS-CoV-2, with a particular focus on post-COVID-19 nephropathy as a new dilemma in the era of nephrology, which can be a new concern for nephrologists that requires more attention and particular strategies

Evaluation of the effects of acupuncture on P6 and anti-gagging acupoints on the gag reflex

Introduction: Hyperactive gag reflex can make dental treatment procedures intolerable for some patients; so, it is highly important for the dentist to control it. Acupuncture is a technique used to control this phenomenon. In this study, the effects of two acupoints, anti-gagging and P6, on the gag r eflex control were analyzed. Materials and Methods: In this clinical trial study, a total number of 100 healthy people were classified into four groups. Acupuncture and psuedo-acupuncture procedures were performed on anti-gagging and P6 points based on the group. Before and after acupuncture or pseudo-acupuncture, gag reflex severity was measured via stimulation of the soft palate, tonsils, and root of the tongue. Obtained data was analyzed using statistical package of social sciences (SPSS) 22 statistical software. Results: Acupuncture reduced gag reflex at both points, but psuedo-acupuncture did not reduce the gag reflex. Moreover, no significant difference was observed between acupuncture on P6 and anti-gagging points. Conclusion: Acupuncture on anti-gagging and P6 points can be effective in controlling the gag reflex during routine dental procedures

Injection of Lidocaine Alone versus Lidocaine plus Dexmedetomidine in Impacted Third Molar Extraction Surgery, a Double-Blind Randomized Control Trial for Postoperative Pain Evaluation

Objectives. Administration of medications such as dexmedetomidine as a topical anesthetic has been suggested in the pain control in dentistry. This double-blind randomized control trial study evaluated postoperative pain and associated factors following impacted third molar extraction surgery. Lidocaine alone was taken as the control and lidocaine plus dexmedetomidine as the intervention. Materials and Methods. Forty patients undergoing mandibular third molar extraction entered the study and were randomly allocated to the control and interventional groups. 0.15 ml of dexmedetomidine was added to each lidocaine cartridge and the drug concentration was adjusted to 15 μg for the intervention group while only lidocaine was used in the control group. A visual analog scale was used to measure and record pain levels at the end of the surgery and 6, 12, and 24 hours after the surgery and number of painkillers taken by the patients after the surgery was also recorded. Results. Pain scores of the intervention group decreased significantly during the surgery and also 6, 12, and 24 hours after the surgery compared to the control group. The pain score was correlated significantly with our intervention during the surgery and also 6 and 12 hours after that (all P value0.05). Conclusion. In patients undergoing molar surgery, administration of a combination of dexmedetomidine and lidocaine is beneficial for the pain control. Clinical Relevance. Compared to the injection of lidocaine alone, combination of dexmedetomidine and lidocaine can be used for a better pain control in molar surgeries

Injection of lidocaine alone versus lidocaine plus dexmedetomidine in impacted third molar extraction surgery, a double-blind randomized control trial for postoperative pain evaluation

[[abstract]]Objectives: Administration of medications such as dexmedetomidine as a topical anesthetic has been suggested in the pain control in dentistry. This double-blind randomized control trial study evaluated postoperative pain and associated factors following impacted third molar extraction surgery. Lidocaine alone was taken as the control and lidocaine plus dexmedetomidine as the intervention. Materials and methods: Forty patients undergoing mandibular third molar extraction entered the study and were randomly allocated to the control and interventional groups. 0.15 ml of dexmedetomidine was added to each lidocaine cartridge and the drug concentration was adjusted to 15 μg for the intervention group while only lidocaine was used in the control group. A visual analog scale was used to measure and record pain levels at the end of the surgery and 6, 12, and 24 hours after the surgery and number of painkillers taken by the patients after the surgery was also recorded. Results: Pain scores of the intervention group decreased significantly during the surgery and also 6, 12, and 24 hours after the surgery compared to the control group. The pain score was correlated significantly with our intervention during the surgery and also 6 and 12 hours after that (all P value 0.05). Conclusion: In patients undergoing molar surgery, administration of a combination of dexmedetomidine and lidocaine is beneficial for the pain control. Clinical Relevance. Compared to the injection of lidocaine alone, combination of dexmedetomidine and lidocaine can be used for a better pain control in molar surgeries

Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT as those with optimum, under-scanned, or over-scanned scan length. (2) Methods: To test our hypothesis, we performed a study with 428 consecutive chest CT examinations (mean age 70 ± 14 years; male:female 190:238) performed at one of the four hospitals. CT examinations from two hospitals were used to train the DL classification algorithms to identify lung apices and bases. The developed algorithms were then tested on the data from the remaining two hospitals. For each CT, we recorded the scan lengths above and below the lung apices and bases. Model performance was assessed with receiver operating characteristics (ROC) analysis. (3) Results: The two DL models for lung apex and bases had high sensitivity, specificity, accuracy, and areas under the curve (AUC) for identifying under-scanning (100%, 99%, 99%, and 0.999 (95% CI 0.996–1.000)) and over-scanning (99%, 99%, 99%, and 0.998 (95%CI 0.992–1.000)). (4) Conclusions: Our DL models can accurately identify markers for missing anatomic coverage and over-scanning in chest CTs

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm

Purpose: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation. Methods: With IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 and March 2022 for the following terms: “motion artifacts”, “respiratory motion”, “technically inadequate”, and “suboptimal” or “limited exam”. All CTPA reports were from two quaternary (Site A, n = 335; B, n = 259) and a community (C, n = 199) healthcare sites. A thoracic radiologist reviewed CT images of all positive hits for motion artifacts (present or absent) and their severity (no diagnostic effect or major diagnostic impairment). Coronal multiplanar images from 793 CTPA exams were de-identified and exported offline into an AI model building prototype (Cognex Vision Pro, Cognex Corporation) to train an AI model to perform two-class classification (“motion” or “no motion”) with data from the three sites (70% training dataset, n = 554; 30% validation dataset, n = 239). Separately, data from Site A and Site C were used for training and validating; testing was performed on the Site B CTPA exams. A five-fold repeated cross-validation was performed to evaluate the model performance with accuracy and receiver operating characteristics analysis (ROC). Results: Among the CTPA images from 793 patients (mean age 63 ± 17 years; 391 males, 402 females), 372 had no motion artifacts, and 421 had substantial motion artifacts. The statistics for the average performance of the AI model after five-fold repeated cross-validation for the two-class classification included 94% sensitivity, 91% specificity, 93% accuracy, and 0.93 area under the ROC curve (AUC: 95% CI 0.89–0.97). Conclusion: The AI model used in this study can successfully identify CTPA exams with diagnostic interpretation limiting motion artifacts in multicenter training and test datasets. Clinical relevance: The AI model used in the study can help alert technologists about the presence of substantial motion artifacts on CTPA, where a repeat image acquisition can help salvage diagnostic information

Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution

Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs

Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings

Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1—not important; 5—critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC). Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner