Improving duplex ultrasound methods for diagnosing functional popliteal artery entrapment syndrome.

OBJECTIVES: Popliteal artery entrapment syndrome (PAES) is a rare condition where musculoskeletal structures compress the popliteal artery (POPA) leading to vascular compromise. This study investigates the effect of dynamic plantar- and dorsi-flexion loading on POPA hemodynamic parameters to develop a robust diagnostic ultrasound-based protocol for diagnosing functional PAES. METHODS: Healthy individuals (n = 20), recreational athletes (n = 20), and symptomatic (n = 20) PAES patients were consented. Triplex ultrasound imaging of lower limb arteries was performed (n = 120 limbs). Proximal and distal POPA's in dorsi-/plantar-flexion, in prone and erect positions, were imaged at rest and flexion. Peak systolic velocities (cm/s) and vessel diameter (antero-posterior, cm) was measured. RESULTS: Distal vessel occlusion was noted across all three groups whilst prone during plantar-flexion (62.7%). POPA occlusion was only noted in the proximal vessel within the patient group (15.8%). When prone, 50% of control (n = 40 limbs), 70% of athletes (n = 40 limbs), and 65% of patients (n = 40 limbs) had distal POPA occlusion in plantar-flexion. When prone, recreational athletes (5%), and patients (12.5%) had distal POPA compression under dorsi-flexion. POPA occlusions with the patient in erect position were only noted in the symptomatic patient group under both dorsi-flexion (15.8%) and plantar-flexion (23.7%). CONCLUSION: Compression of the POPA on ultrasound should not be the sole diagnostic criteria for PAES. POPA compression exists in asymptomatic individuals, primarily under prone plantar-flexion. To reduce false positives, ultrasound-based protocols should focus on scanning patients in the erect position only to diagnose PAES, rather than asymptomatic POPA compression. A distinction should be made between the two.

Rapid Carotid Screening by Duplex: A Prospective Single Centre Assessment.

OBJECTIVE: A novel carotid quick scan (CQS) protocol was developed to rapidly screen for carotid atherosclerosis greater than 50% stenosis in a vascular outpatient setting. This study assessed accuracy and time saved. MATERIAL & METHODS: The CQS was developed by consensus agreement between vascular surgeons and accredited clinical vascular scientists through a modified Delphi technique. The protocol comprised a rapid B-mode then colour flow transverse sweep of the common and internal carotid arteries, with internal carotid artery velocity assessment. One hundred outpatients attending with peripheral artery disease or abdominal aortic aneurysm were recruited. CQS sensitivity, specificity and accuracy was assessed against a conventional full carotid duplex study, performed to UK and ESVS guidelines. RESULTS: Twenty four percent of patients (n = 100) had >50% carotid NASCET stenosis. CQS achieved an excellent accuracy of 96.5% in detecting >50% stenosis when compared to full duplex; Cohen's ƙ = .88, (95%CI .79-.97; P < .001), sensitivity 91.4%, specificity 97.6%, positive predictive value (PPV) 88.9% and negative predictive value (NPV) 98.2%. Median (IQR) time to complete the CQS was 13 sec (±12) per side, compared to 151 sec (±78) per side for the full carotid duplex. In the presence of >50% carotid disease, median CQS time was 25 sec (±31) per side compared to 214 (±104) by full scan. CONCLUSION: CQS as a carotid screening tool is rapid, accurate and acceptable to the population and workforce. It would be simple to roll out in all vascular laboratories to reduce the time and cost burden of excluding significant carotid disease in any group.

A pilot prospective cohort study using experimental quantification of early peripheral nerve regeneration with high-frequency three-dimensional tomographic ultrasound (HFtUS).

Quantification of peripheral nerve regeneration after injury relies upon subjective outcome measures or electrophysiology assessments requiring fully regenerated neurons. Nerve surgeons and researchers lack objective, quantifiable information on the site of surgical repair and regenerative front. To address this need, we developed a quantifiable, visual, clinically available measure of early peripheral nerve regeneration using high-frequency, three-dimensional, tomographic ultrasound (HFtUS). We conducted a prospective, longitudinal study of adult patients with ulnar and/or median nerve injury of the arm undergoing direct epineurial repair within 5 days of injury. Assessment of morphology, volumetric and 3D grey-scale quantification of cross-sectional views were made at baseline up to 15 months post-surgery. Sensory and motor clinical outcome measures and patient reported outcome measures (PROMs) were recorded. Five participants were recruited to the study. Our data demonstrated grey-scale values (an indication of axonal density) increased in distal stumps within 2-4 months after repair, returning to normal as regeneration completed (4-6 months) with concomitant reduction in intraneural volume as surgical oedema resolved. Two patients with abnormal regeneration were characterized by increased intraneural volume and minimal grey-scale change. HFtUS may quantify early peripheral nerve regeneration offering a window of opportunity for surgical intervention where early abnormal regeneration is detected.

Editor's Choice - Meta-Analysis of Compliance with Endovascular Aneurysm Repair Surveillance: The EVAR Surveillance Paradox.

OBJECTIVE: To compare the survival of patients who attended surveillance after endovascular aneurysm repair (EVAR) with those who were non-compliant. DATA SOURCES: MEDLINE and Embase were searched using the Ovid interface. REVIEW METHODS: A systematic review was conducted complying with the PRISMA guidelines. Eligible studies compared survival in EVAR surveillance compliant patients with non-compliant patients. Non-compliance was defined as failure to attend at least one post-EVAR follow up. The risk of bias was assessed with the Newcastle-Ottawa scale, and the certainty of evidence using the GRADE framework. Primary outcomes were survival and aneurysm related death. Effect measures were the hazard ratio (HR) or odds ratio (OR) and 95% confidence interval (CI) calculated using the inverse variance or Mantel-Haenszel statistical method and random effects models. RESULTS: Thirteen cohort studies with a total of 22 762 patients were included. Eight studies were deemed high risk of bias. The pooled proportion of patients who were non-compliant with EVAR surveillance was 43% (95% CI 36 - 51). No statistically significant difference was found in the hazard of all cause mortality (HR 1.04, 95% CI 0.61 - 1.77), aneurysm related mortality (HR 1.80, 95% CI 0.85-3.80), or secondary intervention (HR 0.66, 95% CI 0.31 - 1.41) between patients who had incomplete and complete follow up after EVAR. The odds of aneurysm rupture were lower in non-compliant patients (OR 0.63, 95% CI 0.39 - 1.01). The certainty of evidence was very low for all outcomes. Subgroup analysis for patients who had no surveillance vs. those with complete surveillance showed no significant difference in all cause mortality (HR 1.10, 95% CI 0.43 - 2.80). CONCLUSION: Patients who were non-compliant with EVAR surveillance had similar survival to those who were compliant. These findings question the value of intense surveillance in all patients post-EVAR and highlight the need for further research on individualised or risk adjusted surveillance.

Lower Extremity Amputation and Peripheral Revascularisation Rates in Romania and Their Relationship with Comorbidities and Vascular Care.

(1) Background: This retrospective Romanian study aimed to calculate the rate of, and comparison between, amputation and revascularisation for patients with either cardiovascular or diabetic comorbidities. (2) Materials: In our hospital-based database, we analysed patient-level data from a series of 61 hospitals for 2019, which covers 44.9% of the amputation patients for that year. The national database is compiled by the national houses of insurance and was used to follow amputations and revascularisations between 2016 and 2021. (3) Results: During the six-year period, the mean number of amputations and revascularisations was 72.4 per 100,000 inhabitants per year for both groups. In this period, a decline in open-surgical revascularisation was observed from 58.3% to 47.5% in all interventions but was not statistically significant (r = -0.20, p = 0.70). The mean age of patients with amputation (hospital-based database) was 67 years. Of these patients, only 5.1% underwent revascularisation in the same hospital prior to amputation. The most common comorbidities in those undergoing amputations were peripheral arterial disease (76.8%), diabetes (60.8%), and arterial hypertension (53.5%). Most amputations were undertaken by general surgeons (73.0%) and only a small number of patients were treated by vascular surgeons (17.4%). (4) Conclusions: The signal from our data indicates that Romanian patients probably have a high risk of amputation > 5 years earlier than Western European countries, such as Denmark, Finland, and Germany. The prevalence of revascularisations in Romania is 64% lower than in the Western European countries.

European Society for Vascular Surgery (ESVS) Certification of Theoretical and Practical Competences in Basic Vascular Ultrasound: Validity Investigation of the Assessment Tools.

OBJECTIVE: The aim of this study was to gather validity evidence for the Assessment of basic Vascular Ultrasound Expertise (AVAUSE) tool, and to establish a pass/fail score for each component, to support decisions for certification. METHODS: A cross sectional validation study performed during the European Society for Vascular Surgery's annual meeting. Validity evidence was sought for the theoretical test and two practical tests based on Messick's framework. The participants were vascular surgeons, vascular surgical trainees, sonographers, and nurses with varying experience levels. Five vascular ultrasound experts developed the theoretical and two practical test components of the AVAUSE tool for each test component. Two stations were set up for carotid examinations and two for superficial venous incompetence (SVI) examinations. Eight raters were assigned in pairs to each station. Three methods were used to set pass/fail scores: contrasting groups' method; rater consensus; and extended Angoff. RESULTS: Nineteen participants were enrolled. Acceptable internal consistency reliability (Cronbach's alpha) for the AVAUSE theoretical (0.93), carotid (0.84), and SVI (0.65) practical test were shown. In the carotid examination, inter-rater reliability (IRR) for the two rater pairs was good: 0.68 and 0.78, respectively. The carotid scores correlated significantly with years of experience (Pearson's r = 0.56, p = .013) but not with number of examinations in the last five years. For SVI, IRR was excellent at 0.81 and 0.87. SVI performance scores did not correlate with years of experience and number of examinations. The pass/fail score set by the contrasting groups' method was 29 points out of 50. The rater set pass/fail scores were 3.0 points for both carotid and SVI examinations and were used to determine successful participants. Ten of 19 participants passed the tests and were certified. CONCLUSION: Validity evidence was sought and established for the AVAUSE comprehensive tool, including pass/fail standards. AVAUSE can be used to assess competences in basic vascular ultrasound, allowing operators to progress towards independent practice.

A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.

Early detection of lung nodules is extremely important for the diagnosis and clinical management of lung cancer. In this paper, a novel computer aided detection (CAD) system for the detection of pulmonary nodules in thoracic computed tomography (CT) imagery is presented. The paper describes the architecture of the CAD system and assesses its performance on a publicly available database to serve as a benchmark for future research efforts. Training and tuning of all modules in our CAD system is done using a separate and independent dataset provided courtesy of the University of Texas Medical Branch (UTMB). The publicly available testing dataset is that created by the Lung Image Database Consortium (LIDC). The LIDC data used here is comprised of 84 CT scans containing 143 nodules ranging from 3 to 30mm in effective size that are manually segmented at least by one of the four radiologists. The CAD system uses a fully automated lung segmentation algorithm to define the boundaries of the lung regions. It combines intensity thresholding with morphological processing to detect and segment nodule candidates simultaneously. A set of 245 features is computed for each segmented nodule candidate. A sequential forward selection process is used to determine the optimum subset of features for two distinct classifiers, a Fisher Linear Discriminant (FLD) classifier and a quadratic classifier. A performance comparison between the two classifiers is presented, and based on this, the FLD classifier is selected for the CAD system. With an average of 517.5 nodule candidates per case/scan (517.5+/-72.9), the proposed front-end detector/segmentor is able to detect 92.8% of all the nodules in the LIDC/testing dataset (based on merged ground truth). The mean overlap between the nodule regions delineated by three or more radiologists and the ones segmented by the proposed segmentation algorithm is approximately 63%. Overall, with a specificity of 3 false positives (FPs) per case/patient on average, the CAD system is able to correctly identify 80.4% of the nodules (115/143) using 40 selected features. A 7-fold cross-validation performance analysis using the LIDC database only shows CAD sensitivity of 82.66% with an average of 3 FPs per CT scan/case.

Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs.

A new computer aided detection (CAD) system is presented for the detection of pulmonary nodules on chest radiographs. Here, we present the details of the proposed algorithm and provide a performance analysis using a publicly available database to serve as a benchmark for future research efforts. All aspects of algorithm training were done using an independent dataset containing 167 chest radiographs with a total of 181 lung nodules. The publicly available test set was created by the Standard Digital Image Database Project Team of the Scientific Committee of the Japanese Society of Radiological Technology (JRST). The JRST dataset used here is comprised of 154 chest radiographs containing one radiologist confirmed nodule each (100 malignant cases, 54 benign cases). The CAD system uses an active shape model for anatomical segmentation. This is followed by a new weighted-multiscale convergence-index nodule candidate detector. A novel candidate segmentation algorithm is proposed that uses an adaptive distance-based threshold. A set of 114 features is computed for each candidate. A Fisher linear discriminant (FLD) classifier is used on a subset of 46 features to produce the final detections. Our results indicate that the system is able to detect 78.1% of the nodules in the JRST test set with and average of 4.0 false positives per image (excluding 14 cases containing lung nodules in retrocardiac and subdiaphragmatic regions of the lung).

A computer-aided detection system for the evaluation of breast cancer by mammographic appearance and lesion size.

OBJECTIVE: The purpose of our study was to evaluate the performance of a computer-aided detection (CAD) system in the detection of breast cancer based on mammographic appearance and lesion size. CONCLUSION: The CAD system correctly marked most biopsy-proven breast cancers, with a greater sensitivity for microcalcification than for mass lesions but with no significant difference in performance based on cancer size. CAD was highly effective in detecting even the smallest lesions, with a sensitivity of 92% for lesions of 5 mm or less. CAD is a useful tool for the detection of breast cancer.

Impact of breast density on computer-aided detection for breast cancer.

OBJECTIVE: Our aim was to determine whether breast density affects the performance of a computer-aided detection (CAD) system for the detection of breast cancer. MATERIALS AND METHODS: Nine hundred six sequential mammographically detected breast cancers and 147 normal screening mammograms from 18 facilities were classified by mammographic density. BI-RADS 1 and 2 density cases were classified as nondense breasts; BI-RADS 3 and 4 density cases were classified as dense breasts. Cancers were classified as either masses or microcalcifications. All mammograms from the cancer and normal cases were evaluated by the CAD system. The sensitivity and false-positive rates from CAD in dense and nondense breasts were evaluated and compared. RESULTS: Overall, 809 (89%) of 906 cancer cases were detected by CAD; 455/505 (90%) cancers in nondense breasts and 354/401 (88%) cancers in dense breasts were detected. CAD sensitivity was not affected by breast density (p=0.38). Across both breast density categories, 280/296 (95%) microcalcification cases and 529/610 (87%) mass cases were detected. One hundred fourteen (93%) of the 122 microcalcifications in nondense breasts and 166 (95%) of 174 microcalcifications in dense breasts were detected, showing that CAD sensitivity to microcalcifications is not dependent on breast density (p=0.46). Three hundred forty-one (89%) of 383 masses in nondense breasts, and 188 (83%) of 227 masses in dense breasts were detected-that is, CAD sensitivity to masses is affected by breast density (p=0.03). There were more false-positive marks on dense versus nondense mammograms (p=0.04). CONCLUSION: Breast density does not impact overall CAD detection of breast cancer. There is no statistically significant difference in breast cancer detection in dense and nondense breasts. However, the detection of breast cancer manifesting as masses is impacted by breast density. The false-positive rate is lower in nondense versus dense breasts. CAD may be particularly advantageous in patients with dense breasts, in which mammography is most challenging.

Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

OBJECTIVE: To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. DESIGN: Prospective in vivo animal model of hemorrhagic shock. SETTING: Research foundation animal surgical suite; computer laboratories of collaborating industry partner. SUBJECTS: Nineteen, juvenile, 25- to 35-kg, male and female swine. INTERVENTIONS: Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. MEASUREMENTS AND MAIN RESULTS: Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). CONCLUSIONS: These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal hemorrhagic shock. We suggest that this technology may represent a noninvasive means of assessing the physiologic state during and immediately following hemorrhage. Point of care application of this technology may improve outcomes with earlier diagnosis and better titration of therapy of shock.

Determining efficacy of mammographic CAD systems.

Computer-aided detection (CAD) system sensitivity estimates without a radiologist in the loop are straightforward to measure but are extremely data dependent. The only relevant performance metric is improvement in CAD-assisted radiologist sensitivity. Unfortunately, this is difficult to accurately assess. Without a large study measuring the improvement in CAD-assisted radiologist sensitivity over the same cases, it is not possible to make valid comparisons between systems. As multiple CAD systems become commercially available, comparison issues need to be explored and resolved. Data from clinical trials of 2 systems are examined. Statistical hypothesis tests are applied to these data. Additionally, sensitivities of 2 systems are compared from an experiment testing over the same 120 cases. Even with large databases, there is not sufficient evidence to conclude performance differences exist between the 2 systems. It is prohibitively expensive to show conclusive sensitivity differences between commercially available mammographic CAD systems.