Radiological Interpretation Research Articles

Defining pediatric trauma center resource utilization: Multidisciplinary consensus-based criteria from the Pediatric Trauma Society.

Pediatric trauma triage and transfer decisions should incorporate the likelihood that an injured child will require pediatric trauma center (PTC) resources. Resource utilization may be a better basis than mortality risk when evaluating pediatric injury severity. However, there is currently no consensus definition of PTC resource utilization that encompasses the full scope of PTC services. Consensus criteria were developed in collaboration with the Pediatric Trauma Society (PTS) Research Committee using a modified Delphi approach. An expert panel was recruited representing the following pediatric disciplines: prehospital care, emergency medicine, nursing, general surgery, neurosurgery, orthopedics, anesthesia, radiology, critical care, child abuse, and rehabilitation medicine. Resource utilization criteria were drafted from a comprehensive literature review, seeking to complete the following sentence: "Pediatric patients with traumatic injuries have used PTC resources if they..." Criteria were then refined and underwent three rounds of voting to achieve consensus. Consensus was defined as agreement of 75% or more panelists. Between the second and third voting rounds, broad feedback from attendees of the PTS annual meeting was obtained. The Delphi panel consisted of 18 members from 15 institutions. Twenty initial draft criteria were developed based on literature review. These criteria dealt with airway interventions, vascular access, initial stabilization procedures, fluid resuscitation, blood product transfusion, abdominal trauma/solid organ injury management, intensive care monitoring, anesthesia/sedation, advanced imaging, radiologic interpretation, child abuse evaluation, and rehabilitative services. After refinement and panel voting, 14 criteria achieved the >75% consensus threshold. The final consensus criteria were reviewed and endorsed by the PTS Guidelines Committee. This study defines multidisciplinary consensus-based criteria for PTC resource utilization. These criteria are an important step toward developing a criterion standard, resource-based, pediatric injury severity metric. Such metrics can help optimize system-level pediatric trauma triage based on likelihood of requiring PTC resources. Diagnostic Test/Criteria; Level II.

Artificial intelligence improves transfer times and ischemic stroke workflow metrics.

Rapid initiation of mechanical thrombectomy (MT) for the treatment of large-vessel occlusion (LVO) critically improves patient outcomes. Artificial intelligence algorithms aid in the identification of LVOs and improve door to puncture times as well as patient transfer times. We aimed to determine whether the implementation of an LVO detection algorithm that provides immediate active notification to the thrombectomy team provider's cell phone would improve ischemic stroke workflow at our institution and aid in patient transfer from outlying hospitals when compared to our prior system of passive computed tomography perfusion software analysis and radiologist interpretation and notification. A retrospective review of our institutional thrombectomy registry was performed for all patients who underwent MT between January 2020 and March 2022. Demographic, radiographic, and stroke workflow metrics and notification times were collected. Transfer times and stroke metrics were compared pre- and post-implementation of the Viz.ai (Viz.ai, San Francisco, California, USA) smartphone application. Two hundred sixty-two patients underwent MT during the study period. Door-to-puncture time decreased 15 min (p = 0.009) after the implementation of Viz.ai at our Comprehensive Stroke Center. Transfer time from outside hospitals that implemented Viz.ai was reduced by 37 min (p = 0.04). There was no significant change in transfer time over the same time period in outlying hospitals that did not implement the Viz.ai software. Active notification of the neurosurgical team significantly reduces patient transfer time and initiation of MT.

Applicability of postmortem CT organ-selective volume rendering in evaluating liver and heart injuries

ObjectivesPostmortem computed tomography (PMCT) is widely used in forensic cause-of-death investigations, and 3D volume rendering is commonly employed for visualizing PMCT data. However, no studies have investigated the effectiveness of organ-selective 3D volume rendering for detection of organ injuries such as liver and heart. This study aimed to assess whether selective volume rendering could enhance the detectability of organ damage that is generally difficult to observe in 2DCT images. Material and methodsUsing 22 selected cases of liver or heart injury, the detectability of the injury in 2DCT images and selective volume-rendered images was compared with actual autopsy findings. ResultsAmong 15 cases of liver injury confirmed at autopsy, 2DCT imaging detected two, whereas selective volume-rendered imaging detected one. Furthermore, among 17 cases of cardiac injury, 2DCT imaging detected two, whereas selective volume-rendered imaging detected four. Thus, a small proportion of liver and heart injury cases were detectable using 2DCT imaging or selective volume rendering. In all cases where selective volume rendering identified an injury, the injury was not identified by radiologist interpretation of the 2DCT images, although a review of the 2DCT images did allow identification of the injury when corroborated by the findings of selective volume rendering. One additional case demonstrated the usefulness of selective volume rendering for estimation of puncture wound direction. ConclusionsAlthough selective volume rendering has seldom been effective in detecting heart or liver damage, organ-selective volume rendering may provide clues for detection of injuries that are often overlooked in 2DCT images alone.

A - 84 Neuropsychological Phenotypes of FDG-PET Identified Corticobasal Degeneration Pathology.

Corticobasal degeneration (CBD), a rare neurodegenerative disorder, can present with diverse clinical phenotypes. This qualitative case series examines clinical progression of neuropsychological findings associated with CBD identified pathology on fluorodeoxyglucose-positron emission tomography (FDG-PET). Three clinically referred older adults underwent neuropsychological assessment prior to and after FDG-PET. All patients reported subjective memory complaints. Patient A: 67-year-old female with swallowing difficulty. Patient B: 83-year-old male with decreased balance. Patient C: 77-year-old male with gait disturbance. Neuropsychological re-evaluations occurred at 64, 19, and 17months, respectively. Radiological interpretations of FDG-PET hypometabolism were reviewed. Clinically significant changes were defined as >1 standard deviation discrepancy from baseline performance. Patients A & C had predominant right hemisphere hypometabolism, and Patient B had predominantly left hemisphere hypometabolism. Patient A: hypometabolism for R > L frontoparietal, right temporal and occipital regions. Neuropsychological declines in left fine motor control/speed, attention, visual memory, and semantic fluency. Patient B: hypometabolism for left temporal, frontal, parietal, and occipital regions. Neuropsychological declines in semantic fluency and set-shifting. Patient C: hypometabolism for diffuse right hemisphere, including thalamus and basal ganglia. Neuropsychological declines in verbal learning and memory. There were no significant neuropsychological improvements for Patients A or C. Significant neuropsychological improvement for Patient B was evident for verbal memory and naming. The present case series highlights the diverse clinical presentation associated with CBD identified pathology on FDG-PET. Further studies are needed to characterize clinical phenotypes of CBD as they relate to quantitative FDG-PET pathology.

Potential Applications and Impact of ChatGPT in Radiology

Radiology has always gone hand-in-hand with technology and artificial intelligence (AI) is not new to the field. While various AI devices and algorithms have already been integrated in the daily clinical practice of radiology, with applications ranging from scheduling patient appointments to detecting and diagnosing certain clinical conditions on imaging, the use of natural language processingand large language modelbased software have been in discussion for a long time.Algorithms like ChatGPT can help in improving patient outcomes, increasing the efficiency of radiology interpretation, and aiding in the overall workflow of radiologists and here we discuss some of its potential applications.

Eye tracking validation: Improving radiologist reporting and interpretation

Rationale and objectivesThis study aims to validate a new radiology reporting style using eye tracking to maximize radiologist interpretation time, increase accuracy, and minimize dictation time, ultimately providing a clinically relevant, concise, and accurate reporting style. Materials and methodsThe positive findings only dictation style using a podcast stand-alone microphone (n = 76) was compared with the standard check-list dictation style using a handheld microphone (n = 81). Experienced board-certified radiologists used each style for various imaging modalities. The number of voice recognition corrections per case was tracked. Eye-tracking glasses captured eye movement to document dictation, interpretation, and total examination times. This device also generated thermal heat maps for each style. The statistical difference between the two methods was assessed via descriptive analysis and inferential statistics. ResultsEye tracking revealed that the new positive findings dictation style led to a noteworthy shift in radiologists' visual attention, with reduced heat map overlaying the reporting software compared to the standard check-list style, indicating greater focus on medical images. Cases with at least one voice recognition correction significantly decreased using the positive findings dictation style versus the standard check-list style (5.26 % vs. 14.81 %; p = 0.0240). The positive findings dictation style significantly decreased average dictation time (16.54 s [s] vs. 29.39 s; p = 0.0003) without impacting interpretation time (70.90 s vs. 64.30 s; p = 0.7799) or total examination time (87.45 s vs. 93.69 s; p = 0.3756) compared to the standard style. ConclusionPositive findings only dictation style significantly decreased dictation time and enhanced accuracy without compromising total interpretation time.

Radiomics in the evaluation of ovarian masses — a systematic review

ObjectivesThe study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.MethodsMEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology.ResultsAfter duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was − 16.7%.ConclusionRadiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation.Clinical relevance statementRadiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation.Key points• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses.• Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction.• Modelling with larger cohorts and real-world evaluation is required before clinical translation.Graphical

Evaluating Efficiency of Time Use and Operational Costs in a Breast Clinic Workflow: A Comparative Analysis Between Automated Breast Ultrasound and Handheld Ultrasound.

The aim of this study was to evaluate efficiency of time use for radiologists and operational costs of automated breast ultrasound (ABUS) versus handheld breast ultrasound (HHUS). This study was approved by the Institutional Review Board, and informed consent was waived. One hundred and fifty-three patients, aged 21-81 years, underwent both ABUS and HHUS. The time required for the ABUS scanning and radiologist interpretation and the combined scanning and interpretation time for HHUS were recorded for screening and diagnostic exams. One-Way ANOVA test was used to compare the methods, and Cohen Kappa statistics were used to achieve the agreement levels. Finally, the cost of the methods and return of interest were compared by completing a cost analysis. The overall mean ± standard deviation examination time required for ABUS examination was 676.2±145.42 seconds while mean scan time performed by radiographers was 411.76±67.79 seconds, and the mean radiologist time was 234.01±81.88 seconds. The overall mean examination time required for HHUS was 452.52±171.26 seconds, and the mean scan time and radiologist time were 419.62±143.24 seconds. The reduced time translated into savings of 7.369 TL/month, and savings of 22% in operational costs was achieved with ABUS. The radiologist's time was reduced with ABUS in both screening and diagnostic scenarios. Although a second-look HHUS is required for diagnostic cases, ABUS still saves radiologists time by enabling a focused approach instead of a complete evaluation of both breasts. Thus, ABUS appears to save both medical staff time and operational costs.

Understanding the role of radiologists in complex treatment decisions for patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and represents a significant global health burden. Management of HCC can be challenging due to multiple factors, including variable expectations for treatment outcomes. Several treatment options are available, each with specific eligibility and ineligibility criteria, and are provided by a multidisciplinary team of specialists. Radiologists should be aware of the types of treatment options available, as well as the criteria guiding the development of individualized treatment plans. This awareness enables radiologists to contribute effectively to patient-centered multidisciplinary tumor boards for HCC and play a central role in reassessing care plans when the treatment response is deemed inadequate. This comprehensive review aims to equip radiologists with an overview of HCC staging systems, treatment options, and eligibility criteria. The review also discusses the significance of imaging in HCC diagnosis, treatment planning, and monitoring treatment response. Furthermore, we highlight the crucial branch points in the treatment decision-making process that depend on radiological interpretation.

In Vivo Effect of Partial Ulnar Collateral Ligament Tear Location on Ulnohumeral Joint Gapping With Stress Ultrasound in Baseball Pitchers

Background: Stress ultrasonography (SUS) has provided clinicians with an alternative means of evaluating ulnar collateral ligament (UCL) injury by dynamically assessing ulnohumeral joint space gapping under applied valgus stress. However, 3 previous cadaveric studies have evaluated the biomechanical effect of partial UCL tears at different anatomic locations with conflicting results. Purpose/Hypothesis: The purpose of this study was to use in vivo data from patients with magnetic resonance (MR)–confirmed partial UCL tears to determine if anatomic partial tear location influenced the resultant stability of the elbow in terms of ulnohumeral joint gapping on SUS. It was hypothesized that no difference would be seen in the amount of ulnohumeral joint gapping based on MR-identified anatomic partial UCL tear location. Study Design: Cohort study; Level of evidence, 3. Methods: All patients diagnosed with elbow injury from 2015 to 2020 were screened to identify competitive baseball pitchers with a partial UCL tear who received an SUS. Partial UCL tear was diagnosed via surgeon and radiologist interpretation of elbow MR imaging (MRI) as well as confirmation at the time of surgery as documented in the operative report. Demographic, injury, and treatment data were collected from the clinic notes, MRI reports, and stress ultrasound reports. Ulnohumeral joint gapping on SUS was calculated as the difference between ulnohumeral joint gapping without valgus stress versus ulnohumeral joint gapping with valgus stress (delta) as performed by a musculoskeletal radiologist. Results: Overall, 60 male baseball pitchers (age, 19.2 ± 2.1 years), including 22 (37%) high school, 26 (43%) collegiate, and 12 (20%) professional pitchers, were evaluated. Regarding the location of partial UCL tears, 32 (53%) pitchers had proximal tears and 28 (47%) had distal tears. The mean delta value cohort was 2.1 ± 1.1 mm, and 34 pitchers (57%) had delta values >2.0 mm. Ulnohumeral joint gapping (high school [1.9 mm] vs collegiate [2.6 mm] vs professional [1.6 mm]; P = .004) and the percentage of pitchers with delta values >2.0 mm (45% vs 77% vs 33%; P = .017) differed based on level of competition. After controlling for age, body mass index, and level of competition in a multivariate linear regression, tear location was not related to ulnohumeral joint gapping (P = .499). Conclusion: No difference was observed in the amount of ulnohumeral joint gapping on SUS in symptomatic baseball pitchers with MR-identified partial UCL tears at differing anatomic locations. The findings of this study are contrary to previous cadaveric studies that have evaluated the effects of UCL tear characteristics on delta laxity.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

ObjectiveTo evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history. Materials and MethodsThis study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles. ResultsOf the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0–2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively. ConclusionThe reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Rare giant ovarian metastasis arising from a small primary lung adenocarcinoma: a case report.

This intricate case report details an exceptionally rare incidence of ovarian metastasis originating from a primary lung adenocarcinoma (LUAD). The relative rarity of this metastatic pathway in medical literature indicates significant diagnostic challenges. This patient was initially found to have both the ovarian tumor and lung nodule and they were originally considered independent primary tumors, derived from radiological interpretations and biomarker profiling. Nevertheless, subsequent postoperative histopathological and immunohistochemical staining evaluations identified ovarian tumors as invasive adenocarcinoma metastasized from lung. The lung and ovary tumor both showed marked anaplastic lymphoma kinase gene (ALK) protein expression by immunohistochemistry. The molecular pathologic genetic testing for lung tumor also revealed ALK rearrangement positive. The complexity of this case underscores the essentiality of maintaining a high degree of diagnostic vigilance, particularly when confronting synchronous tumors. In addition, immunohistochemical staining plays an important role in diagnosing the ovarian neoplasm's metastatic nature and determining the primary site of metastatic adenocarcinoma. For lung cancer with ovary metastasis patients, the adopting an adaptable treatment approach responsive to evolving diagnostic evidence can improve the accuracy of diagnosis and avoid excessive treatment of patients.

Exploring the potential for transdisciplinary co-production in the case of nuclear waste disposal (W11)

Abstract. Citizen science and other transdisciplinary approaches become more important in environmental policy and within academia. Scientists benefit from citizens as they generate more socially robust knowledge. In the contested theme of nuclear waste management (NWM), such approaches can be particularly valuable, but they also face challenges. Within our transdisciplinary research project (https://www.transens.de/, last access: 12 June 2023), we work jointly with 14 citizens (citizen working group or CWG) who are interested in the topic without being activists or having a specific agenda. The CWG helps us to enrich the research process with experiential knowledge. Parallel to gaining scientific knowledge, trust can be built due to close collaboration. However, the CWG becomes more scientific over time and no longer resembles the general public. This may have positive effects, as discussions can go deeper into scientific concepts, for instance. However, the increased literacy of a CWG may be criticized for lacking impartiality. Moreover, we established a local measurement lab at the village of Remlingen near the Asse II site. This lab is open to the local public for measuring environmental samples using techniques such as gamma spectroscopy. Experts from the Institute of Radioecology and Radiation Protection (IRS) in Hanover provide assistance concerning technical and radiological questions and appropriate interpretation. One essential aim is to enable citizens to measure their own samples under scientific guidance. During the workshop, we would like to discuss the benefits and challenges of such collaborations, going beyond the aforementioned examples. Potential key questions are as follows: How to ensure the quality of the citizens' work (measurements) and interpretation of results? Participation and in particular co-determination lead to a certain responsibility for the outcome: are the citizens aware of this and willing to assume it? To what extent is extended participation with empowerment for decision-making still in line with representative democracy? Is there a possibility for multiplication and/or upscaling of small-scale approaches? We propose a workshop (90 min) setting with a rich picture methodology (https://naturwissenschaften.ch/co-producing-knowledge-explained/methods/method_factsheets/rich_picture, last access: 12 June 2023). After brief inputs on named examples, we will work in groups of approximately five participants. Participants draw a picture (e.g., on a whiteboard or flip chart) with a focus on the positive results and challenges of citizen participation in research projects. After finishing their pictures, the participants note on cue cards which findings surprised them the most. This is followed by a final discussion.

Construction and Application of Rib Fracture Diagnosis Model Based on YOLOv3 Algorithm.

The artificial intelligence-aided diagnosis model of rib fractures based on YOLOv3 algorithm was established and applied to practical case to explore the application advantages in rib fracture cases in forensic medicine. DICOM format CT images of 884 cases with rib fractures caused by thoracic trauma were collected, and 801 of them were used as training and validation sets. A rib fracture diagnosis model based on YOLOv3 algorithm and Darknet53 as the backbone network was built. After the model was established, 83 cases were taken as the test set, and the precision rate, recall rate, F1-score and radiology interpretation time were calculated. The model was used to diagnose a practical case and compared with manual diagnosis. The established model was used to test 83 cases, the fracture precision rate of this model was 90.5%, the recall rate was 75.4%, F1-score was 0.82, the radiology interpretation time was 4.4 images per second and the identification time of each patient's data was 21 s, much faster than manual diagnosis. The recognition results of the model was consistent with that of the manual diagnosis. The rib fracture diagnosis model in practical case based on YOLOv3 algorithm can quickly and accurately identify fractures, and the model is easy to operate. It can be used as an auxiliary diagnostic technique in forensic clinical identification.

The Role of Radiology in Multidisciplinary Discussion of Patients With Interstitial Lung Diseases.

Radiologists fulfill a vital role in the multidisciplinary care provided to patients with interstitial lung diseases and other diffuse parenchymal lung disorders. The diagnosis of interstitial lung diseases hinges on the consensus of clinical, radiology, and pathology medical subspecialists, but additional expertise from rheumatology, immunology, or hematology can be invaluable. The thin-section computed tomography (CT) features of lung involvement informs the diagnostic approach. Radiologists should be familiar with radiologic methods (including inspiratory/expiratory and prone imaging) and be well versed in the recognition of the CT features of fibrosis, assessment of the overall pattern of lung involvement, and classification according to the latest guidelines. We present a case-based review that highlights examples wherein CT features and subspecialist radiologist interpretation informed the multidisciplinary team consensus diagnosis and care pathways.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

ObjectivesNon-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.MethodsA deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard.ResultsThe model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced.ConclusionsThe assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation.Clinical relevance statementThis study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care.Key Points• This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans.• The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance.• The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

Ultrasound-guided injection through the rotator cuff interval: a clinical perspective of one institution's results and description of technique.

Fluoroscopic injection through the rotator cuff interval (RCI) is a common technique for diagnostic arthrography and therapeutic intervention. Ultrasound approaches through the RCI have been less commonly studied, but there is a growing body of literature. The purpose of this study was to present a standardized technique of ultrasound-guided injection into the glenohumeral joint utilizing the RCI in magnetic resonance imaging (MRI) arthrography (MRA) and to report one medical group's experience with the technique. A retrospective chart review of all ultrasound-guided injections into the glenohumeral joint utilizing the RCI was performed from July 1, 2014 through June 1, 2021. Data were compiled for age, gender, body mass index (BMI), and prior surgery on the shoulder. The primary endpoint was successful administration of intra-articular dilute gadolinium contrast adequate for radiologic interpretation. A total of 487 injections into the glenohumeral joint via the RCI were performed. One hundred and fifty-five patients had previous shoulder surgery, with the remainder naive to intervention. The success rate of injections into the glenohumeral joint was 99.4 %, with only three injections considered unsuccessful. The three unsuccessful injections did not succeed because of a lack of intra-articular contrast media present. This success rate is impressive and promising, particularly when considering that 155 of the patients had previous surgery, which could potentially cause complications, and because these injections were performed over a long period of 7 years. Accessing the RCI under ultrasound guidance is a very successful technique for injection within the glenohumeral joint.

Teleradiology Service is Indispensable in the Indian Healthcare Sector

Teleradiology is an indispensable service in the healthcare sector, offering numerous benefits to healthcare providers, patients, and radiologists. An ideal teleradiology service provider is one with a sturdy infrastructure and technology provisions with a plethora of highly qualified board-certified radiologists who would cater to the needs of accurate, secure, quality, and timely radiologic interpretations with 24/7/365 coverage for small hospitals, clinics, diagnostic imaging centers and emergency rooms. The aim of the article is to give perspective on teleradiology service as an indispensable service in the Indian healthcare sector. The article also emphasizes the key elements an ideal teleradiology service provider should have.

Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.

Absence of Horizontal Fissure, Middle Lobe Fusion and Anomalous Appearance of Mediastinal Surface of Right Lungs: A Rare Case Report

Anatomical variations of lungs in the form of presence of any extra lobe or absence of anatomical lobe, fissures of lungs are important to know to avoid injuries during surgeries related to lungs and associated structures. In this case report absence of middle lobe and horizontal fissure in two right lungs extracted from male cadaver during routine dissection in the Anatomy department, Adesh institute of medical sciences and research, Bathinda, Punjab, India was noted and reported. Every anatomical structure in left side lung of same cadaver was found normal. Also in one abnormal right lung, gross appearance of right lung was looked like left lung. Anatomical knowledge and statistics of every type of variations are important for anatomists for teaching purposes and for surgeons while performing lobectomies, surgical resections of segments of lungs and for radiological interpretations.