Sonography Research Articles

Efficacy of Lung Ultrasound vs Chest X-Ray in Detecting Lung Consolidation and Edema in Premature Infants in the NICU.

BACKGROUND The incidence of lung diseases in premature newborns is significantly higher than in full-term newborns due to their underdeveloped lungs. Ultrasound and X-ray are commonly-used bedside examinations in neonatology. This study primarily compares the efficacy of chest X-ray (CXR) and lung ultrasound (LUS) images in evaluating lung consolidation and edema in premature newborns at Neonatal Intensive Care Units (NICU). MATERIAL AND METHODS A retrospective analysis was conducted on LUS and CXR examination results, along with clinical records of premature newborns admitted to our hospital's NICU from November 1, 2019, to December 31, 2021. CXR and LUS scans were performed on the same newborn within a day. We evaluated the consolidations and edema by interpreting the CXR and LUS images, then compared the findings. RESULTS Out of 75 cases, 34 showed lung consolidations on LUS (45%), while only 14 exhibited consolidations on CXR (19%). The detection rate of consolidations by LUS was significantly higher compared to CXR (34/75 vs 14/75, P<0.001). Differences were observed between the 2 bedside examinations in identifying consolidations, with some cases seen only on LUS. CXR struggled to accurately assess the severity of lung edema visible on LUS, showing significant disparity in detecting interstitial edema (53/75 vs 21/75, P<0.001). CONCLUSIONS LUS outperforms chest CXR for bedside assessment of lung consolidation and edema in premature newborns.

Lung Ultrasound and Regional Heterogeneity: A Bedside Solution to an Underrecognized Problem?

Lung Ultrasound and Regional Heterogeneity: A Bedside Solution to an Underrecognized Problem?

EP142 - "Unravelling the Enigma: Navigating the Diagnostic Dilemma of Abdominal Toxoplasmosis"

Abstract Introduction Abdominal toxoplasmosis, an infrequent yet medically significant manifestation of Toxoplasma infection, primarily caused by the protozoan parasite Toxoplasma gondii. Although toxoplasmosis can affect various organs, its occurrence in the abdominal region introduces unique diagnostic complexities, presenting symptoms such as abdominal pain, gastrointestinal disruptions, and systemic complications. Case Report In a notable case, a middle-aged patient presented with perplexing gastrointestinal symptoms, including nausea, vomiting, abdominal pain, and irregular bowel habits. With a history of anaemia and thrombocytopenia necessitating transfusions and yielding inconclusive bone marrow biopsies, the patient underwent OGD and colonoscopy with biopsies during initial management, revealing a hiatus hernia and diverticulosis that did not explain the symptoms. Despite being discharged pending biopsy results, subsequent normal findings failed to clarify. Upon readmission with persistent gastrointestinal complaints, a CTAP revealed two suspicious neoplastic focal areas of small bowel wall thickening and enlarged mesenteric lymph nodes. The MDT discussions ensued to rule out malignancy, leading to a follow-up PET scan suggesting nodal disease, raising concerns about lymphoma, small bowel adenocarcinoma, or carcinoid tumour and recommended an ultrasound-guided lymph node biopsy, 5 HIAA if asymptomatic, and diagnostic laparoscopy if symptomatic or obstructed. Subsequent core biopsy of a neck lymph node unveiled appearances consistent with toxoplasmosis, prompting treatment with anti-parasitic drugs. The patient awaits capsule endoscopy, and a follow-up CT scan displayed a reduction in bowel lesions and lymph nodes. Conclusion This case highlights the swift and precise identification of toxoplasmosis, resulting in an optimal patient outcome. The clinical manifestations of toxoplasmosis are diverse and lack distinct characteristics, making it challenging to definitively diagnose, particularly in immunocompetent individuals.

WP2.4 - Association between Gallbladder Polyp Size and Risk of Malignancy: A single centre retrospective study

Abstract Aims To investigate ultrasound image findings in patients with gallbladder polyps requiring cholecystectomy and assess the risk of gallbladder malignancy associated with polyps. Methods Patients with ultrasound diagnosis of gallbladder polyps who underwent cholecystectomy between January 2019 and September 2023 were included. Histology reports were thoroughly reviewed to find possible diagnosis of gallbladder malignancy. Results A total of 701 patients’ records who had cholecystectomy were reviewed, 45 patients were included as per the inclusion criteria, of whom 73% were women. Mean age was 52±15 years. Average size of the polyps on ultrasound was 7.26mm and approximately 26.7% of polyps were noted to be bigger than 10mm. Overall analysis of the data showed that none of the histology confirmed malignancy. Conclusion Literature review and current ESGAR guidelines suggest that gallbladder polyps, particularly with size &amp;gt;10mm have significant risk of conversion to malignancy. Nearly a quarter of the patients who underwent cholecystectomy for gallbladder polyp had a polyp size &amp;gt;10mm however none of them were proven malignant on histology. This study was conducted on small cohort of patients, further studies on larger cohorts are needed in order to draw conclusions.

WP3.1 - Retrospective analysis of gallbladder polyps: correlation between ultrasound imaging and histology findings – a single center study

Abstract Aims To investigate ultrasound imaging findings in patients with gallbladder polyps requiring cholecystectomy and compare them with histopathological results. Methods Patients with ultrasound diagnosis of gallbladder polyps who underwent cholecystectomy between January 2019 and September 2023 were included. Results A total of 701 patients’ records who had cholecystectomy were reviewed, 45 patients were included as per the inclusion criteria, of whom 73% were women. Mean age was 52±15 years. Average size of the polyps on ultrasound was 7.26mm and approximately 26.7% of polyps were noted to be bigger than 10mm. Histopathological findings confirmed the presence of polyps in only 46.7%. Out of these, size of the polyps was documented in 62% of histology reports which was compared with ultrasound findings. Mean size on USG was 9.13±2.73mm in comparison to histological size of 6±2.94mm. Mean difference in measurements was 3.13mm. Conclusion This study examines the relationship between ultrasound imaging and histological findings of gallbladder polyps. From our 45 patients identified with gallbladder polyps the majority were women. Larger sized polyps are more likely to be confirmed on histology and our findings suggest that ultrasound can overestimate polyp-size by up to 3mm. Finally, 53.3% of polyps diagnosed via ultrasound were not confirmed on histological examination. Further studies on larger cohorts are required to validate above findings and refine diagnostic protocols.

Breast Ultrasound Computer-Aided Diagnosis System Based on Mass Irregularity Features in Frequency Domain

Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices’ accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method’s superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings.

Risk of malignancy in thyroid nodules with increased 11C-Choline uptake detected incidentally on PET/CT: A diagnostic accuracy study.

The purpose was to evaluate the pathological nature of focal thyroid uptake seen in 11C-Choline PET/CT performed for prostate cancer. The study was IRB-approved. All 11C-Choline PET/CT exam reports for studies performed between January 01, 2018, and July 30, 2021, in male patients with prostate cancer in our institution were retrospectively reviewed. Exams with "focal thyroid uptake" on their final report were selected. Patients with surgery or ablation in the thyroid prior to the PET/CT, proven parathyroid adenomas or absent thyroid ultrasound were excluded. Repeated PET/CT exams of same patient were excluded. PET images were analyzed visually and semi-quantitatively by measuring the maximum standardized uptake value (SUVmax) of the focal thyroid uptake. Available thyroid ultrasound images, cytology and pathology reports were reviewed. Statistical analyses were performed. Out of 10,047 sequential 11C-Choline PET/CT studies, 318 reports included "focal thyroid uptake." About 128 of these studies were repeat exams and were excluded. Additional 87 patients were excluded, because the uptake was determined to be adjacent, rather than confined to the thyroid gland. Out of the remaining 103 patients, 74 patients had focal thyroid uptake and concurrent thyroid sonographic evaluation. Out of the 74 focal uptakes evaluated with ultrasound, 21 were presumed benign thyroid nodules based on the ultrasound and 53 had further evaluation with biopsy. Sixty three nodules were benign (21 presumed benign on ultrasound and 42 cytology or surgical pathology-proven), 9 nodules were malignant and 2 remained indeterminate. There was no significant difference between the SUVs of the benign and malignant groups (P > .3). In this retrospective study of patients with prostate cancer who underwent 11C-Choline PET/CT, we identified a group of patients who underwent thyroid ultrasound for incidental finding of focal 11C-Choline thyroid uptake. Incidence of malignancy in this group was 12%. Therefore, further investigation with ultrasound and possibly ultrasound-guided biopsy may be warranted when a choline avid thyroid nodule is found incidentally on choline PET.

What To Do With Suspected Nodal Regrowth on Magnetic Resonance Imaging During Follow-up in an Organ Preservation Approach for Rectal Cancer?

BACKGROUND: For nodal regrowth in patients with rectal cancer following watch-and-wait standardized protocols on diagnostic procedures and subsequent treatment are lacking. OBJECTIVE: Evaluate the diagnosis and treatment of suspected nodal regrowth following an organ preservation approach. SETTINGS: Patients were included from national and institutional watch-and-wait -databases. DESIGN: Thirty-five rectal cancer patients with suspected nodal regrowth on magnetic resonance imaging were retrospectively identified during watch-and-wait follow-up. PATIENTS: Twenty-seven of 35 patients followed watch-and-wait after neoadjuvant (chemo)radiotherapy and 8/35 followed watch-and-wait schedule after local excision for early rectal cancer. MAIN OUTCOME MEASURES: Diagnostic procedures, treatment and histopathological outcome. RESULTS: Median follow-up was 34 months. Median time from end of (chemo)radiotherapy or local excision to first detection of suspected nodal regrowth on magnetic resonance imaging following watch-and-wait was 9 and 10 months. After first detection, 17 of 35 patients underwent immediate treatment without further diagnostics, of whom 7 also had luminal regrowth. In 18 of 35 patients, additional diagnostic procedures were performed. In 4 of 18 patients, positron emission tomography-computed tomography or endorectal ultrasound-guided biopsy was performed and treatment was initiated based on increased nodal regrowth suspicion. In 14 of 18 patients, MRI was repeated after 8-12 weeks: growth of suspected lymph nodes was the most decisive factor to proceed to treatment. In 8 patients, repeated magnetic resonance imaging was combined with positron emission tomography-computed tomography and/or endorectal ultrasound-guided biopsy: in half of them it contributed to treatment initiation. In total, 34/35 patients were treated: 9 received (re-)irradiation and 33 underwent total mesorectal excision. In 27 of 33 patients, nodal regrowth was pathologically confirmed in the total mesorectal excision-resection specimen; 5 of 6 patients without nodal involvement had pathologically confirmed luminal regrowth. LIMITATIONS: Highly selected study population. CONCLUSIONS: During watch-and-wait follow-up of patients with rectal cancer in an organ preservation strategy, magnetic resonance imaging plays an important role in diagnosis of nodal regrowth. Repeated magnetic resonance imaging after an interval can be helpful in making treatment decisions, and the role of positron emission tomography-computed tomography and endorectal ultrasound-guided biopsy appears limited. See Video Abstract.

Association of Time Since Diagnosis of Pediatric Ileocolic Intussusception With Success of Attempted Reduction: Analysis in 1065 Patients.

BACKGROUND: Radiologists generally treat pediatric ileocolic intussusceptions emergently given potential worse outcomes from delayed reduction attempts. However, relevant literature is conflicting. OBJECTIVE: To identify factors associated with successful image-guided ileocolic intussusception reduction in children, with attention to the time interval since diagnosis. METHODS: This retrospective study included patients <6 years old who underwent attempted image-guided enema reduction of ileocolic intussusception between May 2009 and July 2023. Patients were separated into two groups: those presenting directly to the institution (nontransferred patients, who all underwent attempted reduction <8 hours after ultrasound diagnosis), and those transferred from outside facilities. EHR data were extracted. Each patient's first image-guided reduction attempt was classified as successful or unsuccessful. Univariable and multivariable analyses were performed. RESULTS: The study included 1065 patients (649 male, 416 female; mean age, 18.1 months; age range, 2.2-71.0 months; 793 nontransferred and 272 transferred patients). In nontransferred patients, the mean interval between ultrasound diagnosis and reduction attempt was 150.8 minutes; in transferred patients, the mean interval between outside facility advanced imaging and reduction attempt was 460.1 minutes (p<.001). Successful reduction occurred in 84.6% and 81.6% of nontransferred and transferred patients, respectively (p=.25). In nontransferred patients, success occurred in 85.6% of attempts <2 hours after diagnosis versus 84.0% of attempts 2-<8 hours after diagnosis (p=.54); the mean interval from diagnosis to attempted reduction was 149.7 minutes and 156.8 minutes for successful and unsuccessful attempts, respectively (p=.53). In multivariable analysis, factors showing independent associations with success were proximal intussusception location (OR=3.63, p<.001) and absence of high-risk ultrasound findings (OR=2.57, p<.001); success was not independently associated with age, sex, bloody stools, reduction method, or time since diagnosis <2 hours (p>.05). In transferred patients, the mean interval from outside advanced imaging to attempted reduction was 463.1 minutes and 440.2 minutes for successful and unsuccessful attempts, respectively (p=.74). CONCLUSION: Intussusception reduction may not require completion emergently (within 2 hours after diagnosis), but potentially may be safely performed on an urgent basis (within 8 hours). CLINICAL IMPACT: The findings have implications for determining the standard of care, including criteria for oncall activation of radiologic resources, in pediatric intussusception management.

Deep learning-based ultrasonographic classification of canine chronic kidney disease.

In veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis. This study aimed to develop a deep learning-based model for classifying the status of canine chronic kidney disease (CKD) using renal ultrasonographic images and assess its diagnostic performance in comparison with that of veterinary imaging specialists, thereby verifying its clinical utility. In this study, 883 ultrasonograms were obtained from 198 dogs, including those diagnosed with CKD according to the International Renal Interest Society (IRIS) guidelines and healthy dogs. After preprocessing and labeling each image with its corresponding IRIS stage, the renal regions were extracted and classified based on the IRIS stage using the convolutional neural network-based object detection algorithm You Only Look Once. The training scenarios consisted of multi-class classification, categorization of images into IRIS stages, and four binary classifications based on specific IRIS stages. To prevent model overfitting, we balanced the dataset, implemented early stopping, used lightweight models, and applied dropout techniques. Model performance was assessed using accuracy, recall, precision, F1 score, and receiver operating characteristic curve and compared with the diagnostic accuracy of four specialists. Inter- and intra-observer variabilities among specialists were also evaluated. The developed model exhibited a low accuracy of 0.46 in multi-class classification. However, a significant performance improvement was observed in binary classifications, with the model designed to distinguish stage 3 or higher showing the highest accuracy of 0.85. In this classification, recall, precision, and F1 score values were all 0.85, and the area under the curve was 0.89. Compared with radiologists, whose accuracy ranged from 0.48 to 0.62 in this experimental scenario, the AI model exhibited superiority. Intra-observer reliability among radiologists was substantial, whereas inter-observer variability showed a moderate level of agreement. This study developed a deep-learning framework capable of reliably classifying CKD IRIS stages 3 and 4 in dogs using ultrasonograms. The developed framework demonstrated higher accuracy than veterinary imaging specialists and provided more objective and consistent interpretations. Therefore, deep-learning-based ultrasound diagnostics are potentially valuable tools for diagnosing CKD in dogs.

Combat Medic-Performed Auscultation Versus Thoracic Ultrasound Image Interpretation for Pneumothorax Detection: Look or Listen?

Background Pneumothorax (PTX) is a potentially life-threatening condition encountered by U.S. Army combat medics on the battlefield. PTX, when left untreated, can progress to tension PTX, a leading cause of preventable death on the battlefield which can be difficult to diagnose based on physical exam alone due to variable physical exam findings. Prior literature shows medics can accurately use point-of-care ultrasound (POCUS) to diagnose PTX in cadaver and porcine models; however, no studies have directly compared the diagnostic accuracy of ultrasound (US) and physical exams performed by this population. We sought to compare medic diagnostic accuracy of a simulated PTX using these two diagnostic modalities. Methodology We conducted a prospective, observational study in which medics from the Flight Paramedic Program at Joint Base San Antonio-Sam Houston, TX received a standardized 30-minute training on physical exam and US diagnosis of PTX, followed by a 20-minute hands-on US familiarization session. Participants were then randomly selected into cohorts to evaluate 12 high-fidelity manikin lung fields and 12 thoracic US video clips for the presence of PTX in a simulated combat environment. Cohorts evaluated the same manikins and thoracic US video clips but in opposing sequences. Our primary outcome compared the sensitivity and specificity of PTX identification via thoracic US image interpretation and physical exam. Results In total, 21 medics evaluated 252 hemithoraces and interpreted 252 thoracic US images.We found a statistically significant difference favoring medics' sensitivity with US image interpretation over physical exam to detect PTX (85.7% vs. 72.4%, p = 0.004). There was no statistically significant difference in specificity between these modalities (81.9%vs. 69.4%, p = 0.139). Conclusions After brief training, medics demonstrated greater sensitivity to detect PTX using thoracic US image interpretation compared to physical exam in a simulated combat environment.Further studies involving medics' ultrasound image acquisition capability in human patients in austere combat environments are warranted.

Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two-step strategy using benign descriptors and ADNEX model: Portuguese multicenter study.

To externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in a Portuguese population, comparing these approaches with subjective assessment and the risk-of-malignancy index (RMI), as well as with each other. This study also aimed to retrospectively validate the IOTA two-step strategy, using modified benign simple descriptors (MBDs) followed by the ADNEX model in cases in which MBDs were not applicable. This was a prospective multicenter diagnostic accuracy study conducted between January 2016 and December 2021 of consecutive patients with an ultrasound diagnosis of at least one adnexal tumor, who underwent surgery at one of three tertiary referral centers in Lisbon, Portugal. All ultrasound assessments were performed by Level-II or -III sonologists with IOTA certification. Patient clinical data and serum CA 125 levels were collected from hospital databases. Each adnexal mass was classified as benign or malignant using subjective assessment, RMI, IOTA SRs, LR2 and the ADNEX model (with and without CA 125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were classified retrospectively using the two-step strategy (MBDs + ADNEX). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios and overall accuracy were determined for all methods. Receiver-operating-characteristics curves were constructed and corresponding areas under the curve (AUC) were determined for RMI, LR2, the ADNEX model and the two-step strategy. The ADNEX model calibration plots were constructed using locally estimated scatterplot smoothing (LOESS). Of the 571 patients included in the study, 428 had benign disease and 143 had malignant disease (prevalence of malignancy, 25.0%), of which 42 had borderline ovarian tumor, 93 had primary invasive adnexal cancer and eight had metastatic tumors in the adnexa. Subjective assessment had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and specificity of 98.6%. The IOTA LR2 had a sensitivity of 84.6%, specificity of 86.9% and an AUC of 0.939, at a malignancy risk cut-off of 10%. At the same cut-off, the sensitivity, specificity and AUC for the ADNEX model with vs without CA 125 were 95.8% vs 98.6%, 82.5% vs 79.7% and 0.962 vs 0.960, respectively. The ADNEX model gave heterogeneous results for distinguishing between benign masses and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary invasive adnexal cancer Stages II-IV, and the lowest AUC (0.696) for discriminating primary invasive adnexal cancer Stage I from metastatic lesion in the adnexa. The calibration plot suggested underestimation of the risk by the ADNEX model compared with the observed proportion of malignancy. The MBDs were applicable in 26.3% (150/571) of cases, of which none was malignant. The two-step strategy using the ADNEX model in the second step only, with and without CA 125, had AUCs of 0.964 and 0.961, respectively, which was similar to applying the ADNEX model in all patients. The IOTA methods showed good-to-excellent performance in the Portuguese population, outperforming RMI. The ADNEX model was superior to other methods in terms of accuracy, but interpretation of its ability to distinguish between malignant subtypes was limited by sample size and large differences in the prevalence of tumor subtypes. The IOTA MBDs are reliable in identifying benign disease. The two-step strategy comprising application of MBDs followed by the ADNEX model if MBDs are not applicable, is suitable for daily clinical practice, circumventing the need to calculate the risk of malignancy in all patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Frequency and Risk Factors of Ultrasound-Guided Kidney Biopsy Complications

Frequency and Risk Factors of Ultrasound-Guided Kidney Biopsy Complications

Is a two-hour monitoring period sufficient and safe for patients undergoing ultrasound-guided percutaneous liver mass biopsy?: A prospective and multicenter experience.

To investigate whether patients undergoing percutaneous liver mass biopsy (PLMB) can be safely discharged following a two-hour monitoring period. A multi-center prospective analysis was conducted for 375 patients (196 males and 179 females), mean age 63 ± 12.45 years (range 37-89) who underwent PLMB between August 2023 and March 2024. Patients were monitored for 24 h, and complications were classified as minor or major. The timing of complications was categorized into three groups: within the first two hours, between the 2nd and 24th hours, and within 1 week after 24 hours. Minor complications occurred in 18.93% (71/375) and major complications in 2.13% (8/375). Most minor complications (80.2%, 57/71) appeared within the first two hours, 12.7% (9/71) between 2 and 24 h, and 7.1% (5/71) after 24 h. All major complications (62.5%, 5/8) except late-onset cases, occurred within the first two hours. No major complications occurred between 2 and 24 h. Late-onset major complications occurred in 37.5% (3/8) after 24 h. The two-hour monitoring period did not adversely impact patient management regarding minor complications and is safe for identifying all major complications except for late-onset ones. Extending the post-biopsy recovery period does not significantly improve patient care.

Ultrasound Diagnosis of Clavicle Fractures in Newborns: A Systematic Review.

Fractures of the clavicle are the most common birth injury among newborns. Aim of this systematic review was to provide a comprehensive analysis of the role of ultrasound (US) in diagnosing clavicular fractures in neonates. A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) using PubMed and Embase, including studies focusing on US in neonatal clavicle fracture. Age at US, number of cases examined by US and X-ray, US and X-ray diagnoses, US probe used, fracture site were systematically extracted. A total of 231 articles were found. We ultimately selected 7 publications that satisfied the inclusion criteria, involving 136 patients examined between 3 days and 3 weeks of age, with 135 confirmed fractures. US was performed on all patients and correctly diagnosed all fractures (135/135, 100%). X-ray was performed on 94/136 patients (69.1%) and correctly diagnosed 89/93 fractures (95.7%). Fracture site was: medial in 2/79, middle in 37/79, and lateral in 40/79. In the remaining 57 cases, site was not reported. This review indicates that ultrasound is extremely reliable in diagnosing clavicle fractures in newborns and should be considered as the gold standard in this context.

Optimizing Real-Time Advanced Ultrasound Supervision in a University Setting

Introduction: Ultrasound-based diagnosis requires great expertise for reliability and accuracy, thus practical training is vital. Digital training can offer students the possibility of practical training in their local setups. However, there are critical issues that remote educators must address, particularly in advanced ultrasound training. The present study was therefore aiming at developing a suitable technical setup for supervision and education in tele-ultrasound that can be applied to a variety of advanced diagnostic and therapeutic areas. Methods: Using an action research approach including four cycles of action, we tested four technical setups with different software and hardware components across four ultrasound courses. Based on evaluation forms, written reflections, and two focus group interviews from the four cycles of action, respectively, we modified the setups to improve the technical solution for the next cycle. Results: The initial set-up was using commercial video call apps via mobile phones. Although no additional equipment or expertise was required, it was precluded by the poor image quality and motion blur resulting from capturing the ultrasound image on the smartphone. Nevertheless, this solution is viable when quick clarifications are needed. Additional stabilization support in the second set-up did not provide satisfactory image quality. The best technical solution was found in the third set-up, using a frame grabber and Reacts, which allows simultaneous live streaming of the ultrasound screen image and two web cameras. However, the cumbersome software interface, the need for an expensive user license, and problems with the internet connection reduced the acceptability of this setup. The optimal setup in cycle four, was a reasonable frame grabber, and Zoom, which is free, has a simple user interface and can stream live audio-visual ultrasound data from the probe and user simultaneously. Its annotation function is also beneficial in supervision. Conclusion: Through an action research approach including four cycles of action and thorough, subsequent evaluations, we found that a videoconferencing platform, such as Zoom, a computer, and a frame grabber connected to the ultrasound scanner was the optimal solution as this set-up is simple and affordable and is facilitating a good video-transmitted learning situation for students and supervisors. This technical set-up could therefore be suitable for a wide range of remote ultrasound courses.

Lung ultrasound in children with primary ciliary dyskinesia or cystic fibrosis.

Primary ciliary dyskinesia (PCD) and cystic fibrosis (CF) are respiratory conditions requiring regular chest radiography (CXR) surveillance to monitor pulmonary disease. However, CXR is insensitive for lung disease in CF and PCD. Lung ultrasound (LU) is a radiation-free alternative showing good correlation with severity of lung disease in CF but has not been studied in PCD. Standardized, six-zone LU studies and CXR were performed on a convenience sample of children with PCD or CF during a single visit when well. LU studies were graded using the LU scoring system, while CXR studies received a modified Chrispin-Norman score. Scores were correlated with clinical outcomes. Data from 30 patients with PCD and 30 with CF (median age PCD 11.5 years, CF 9.1 years) with overall mild pulmonary disease (PCD median FEV1 90% predicted, CF FEV1 100%) were analyzed. LU abnormalities appear in 11/30 (36%) patients with PCD and 9/30 (30%) with CF. Sensitivity, specificity, positive predictive, and negative predictive values for abnormal LU compared to the gold standard of CXR are 42%, 61%, 42%, and 61% in PCD, and 44%, 81%, 50%, and 77% in CF, respectively. Correlation between LU and CXR scores are poor for both diseases (PCD r = -0.1288, p = 0.4977; CF r = 0.0343, p = 0.8571), and LU score does not correlate with clinical outcomes in PCD. The correlation of LU findings with CXR surveillance studies is poor in patients with mild disease burdens from PCD or CF, and LU scores do not correlate with clinical outcomes in PCD.

Advancing lung ultrasound imaging: advanced pre-processing and neoteric segmentation for improved accuracy

Abstract Within the medical imaging domain, the segmentation of Lung Ultrasound (LUS) images is crucial for accurately defining regions of interest, particularly for lung health assessments. various techniques such as threshold-based, region-based, and edge-based methods have been employed to address this challenge, their accuracy and efficiency are often questioned. This highlights the need for an innovative approach. With this context in mind, this research introduces a novel preprocessing step using Optimized Gaussian Histogram Equalization (GHE) followed by a Neoteric Segmentation (NS) approach. This combination enhances the capability of segmenting specific regions within LUS images, regardless of the overarching medical scenario. The primary focus of this method is to understand, through a thorough evaluation, how neoteric segmentation compares with more conventional methods. By conducting an in-depth comparative analysis, valuable insights can be unearthed. The results demonstrate that the optimized GHE technique significantly enhances contrast and preserves visual quality, while the NS approach shows superior performance in accurately segmenting the lung regions. Initial findings suggest that the NS method, achieving an impressive 95% accuracy, holds significant promise and could influence the trajectory of healthcare practices. This research aims to advance the field of medical image analysis by highlighting the potential benefits of this cutting-edge segmentation technique. These advancements could positively impact healthcare outcomes, especially in contexts that leverage lung ultrasound imaging.

Evaluation of Needles in Endoscopic Ultrasound-Guided Tissue Acquisition of Pancreatic Cancer for Genetic Yield and Quality.

Endoscopic ultrasound-guided fine needle biopsy (FNB) is the gold standard in tissue acquisition of pancreatic ductal adenocarcinoma (PDAC). There is a paucity of evidence of the impact of needle type or size on the genetic yield and quality. Patients 18 years and older with PDAC who underwent FNB were retrospectively identified from a single database from 2016 to 2021. Genetic quantity is measured in micrograms (µg) and quality defined by RNA or DNA integrity number (RIN and DIN). FNB needles examined were Acquire 22 gauge (Boston Scientific, Marlborough, MA, USA) and ProCore 22 and 20 gauges (Cook Medical, Bloomington, IN, USA). Two hundred seventy-seven patients were identified. ProCore 20G needle procured higher RNA quantity (4125.8µg, IQR: 2003.8, 5954.8, p = 0.012) compared to ProCore 22G (2050µg IQR: 966.4, 3181.6) and Acquire 22G (2310.6µg, IQR: 1439.3, 4312). Median DNA quantity was 3340.5µg (Acquire 22G), 2610.4µg (ProCore 22G) and 3499.7µg (ProCore 20G) (p = 0.763). Median DIN was 7.3 (Acquire 22G and ProCore 22G) and 7.4 (ProCore 20G) (p = 0.449). Median RIN was 3.0 (Acquire 22G and ProCore 22G) and 2.7 (ProCore 20G) (p = 0.886). ProCore 20G was associated with higher quantity of RNA. There were no differences in the quality acquired by different needles.

Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI

Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model’s predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model’s decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.