Percentage Of False Positives Research Articles

The accuracy of respiratory muscle strength in identifying systolic dysfunction in patients with Chagas cardiomyopathy

Purpose To verify the accuracy of respiratory muscle strength in identifying systolic dysfunction in patients with Chagas cardiomyopathy (ChC), and to validate optimal cutoff points based on respiratory muscle strength. Methods First, 72 patients with ChC were enrolled and underwent echocardiography and assessment of respiratory muscle strength by manovacuometry. Inspiratory and expiratory muscle strength was defined by maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP), respectively. Systolic dysfunction was defined by left ventricular ejection fraction (LVEF) values below 52% (for men) or 54% (for women). Then, the validation of the cutoff points was verified by the percentage of true and false positives in another 30 ChC patients. Results The MIP showed adequate accuracy (p = 0.004) in identifying patients with systolic dysfunction (AUC = 0.73). The MEP did not show satisfactory accuracy in identifying those patients. The optimal MIP cutoff point to identify systolic dysfunction in ChC was ≤62 cmH2O, with a positive predictive value of 87%. In the validation analysis, MIP values below 62 cmH2O were able to identify 77% of patients with systolic dysfunction. Conclusion MIP has potential value in identifying systolic dysfunction in patients with ChC. This finding may aid in screening and risk stratification when echocardiography is not available.

Burst-classifier: Automated classification of solar radio burst type II, III and IV for CALLISTO spectra using physical properties during maximum of solar cycle 24

Continuous observation of solar radio bursts (SRBs) throughout the year using the CALLISTO spectrometer generates a huge volume of spectral data. This study introduces a burst-classifier algorithm, which is an automated algorithm, to classify the SRB spectrum into three solar radio bursts, namely Type II (SRBT II), Type III (SRBT III) and Type IV (SRBT IV). The proposed algorithm was designed using four characteristic parameters derived from a collection of training dataset files. The characteristic parameters were derived from the intensity bursts observed on frequency channels and timesteps of the spectrum. This dataset consisted of 50 spectra of SRBT II and SRBT III, along with 40 spectra for SRBT IV, collected during the solar maximum of 2014 (Solar Cycle 24). After observations and analysis of the training dataset, each burst type was set up with a threshold. A training dataset of 80 data spectra from 2013 to 2016 was used to test the algorithm. Accuracy of the proposed algorithm was calculated using the percentage of true positives (TP) and false positives (FP). Findings demonstrate an accuracy of ∼74 % with 57 out of 80 spectra classified as TP and 23 spectra as FP.

Evaluating legal age of 18 years through observation of third molars using Gambier et al. method in an orthopantomographic sample of subadults from South India

In forensic practice, medicolegal physicians are often tasked with estimating age using dental evidence. This calls for an uncomplicated, reliable, and reproducible method for dental age estimation, enabling physicians to proceed without specific odontological expertise. Among various dental methods, third molar eruption analyses are less complicated and easier to perform. In our study, we explored the effectiveness of Gambier et al.'s scoring system, which examines the eruption of all third molars. We retrospectively analysed 1032 orthopantomograms (528 males and 504 females) of individuals aged between 15 and 24 years. The mean chronological age increased with the progression of stages (1 to 3) and phases (A to D) of the third molar eruption for both sexes. In terms of stages, none showed significant discrimination between minors (<18 years) and adults (>18 years), especially for males. However, Gambier’s phase D displayed a relatively high likelihood of being 18 years or older, with an overall 85.9 % of males and 95.7 % of females having all third molars in stage 3 being 18 years or older. While the tested method could be helpful in indicating the completion of the 18th year of life, caution is advised (due to a high percentage of false positives), and it should be used alongside other age assessment methods by experts.

Identifying Student Misconceptions on Momentum and Impulse Using Four-Tier Diagnostic Test Instrument with CRI

Identification of students' misconceptions is crucial in Physics learning as an evaluation tool to address and reduce these misconceptions. This study aims to assess the quality of a four-tier diagnostic test using the CRI instrument, assisted by JotForm, and describe the profile of students' misconceptions related to momentum and impulse. The quality of the instrument is evaluated based with validity and reliability. The research follows the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) method. The instrument is developed during the analysis, design, and development stages. The implementation and evaluation stages involve capturing the profile and causes of students' misconceptions. The instrument's quality is assessed based on theoretical validity, which reached 89.29%. Empirical content validity haved percentages of false positives 6.93%and false negatives 6.53%. The empirical validity of the construct, with a significance level of 0.05, resulted in an rtableof 0.273. From the 15 questions tested, 12 questions were found to have empirical validity, and a reliability score of 0.475 was obtained. The study revealed that 3 students had low misconceptions, 14 students had medium misconceptions, and 6 students had high misconceptions. The causes of students' misconceptions were primarily false positives and humanistic thinking, accounting for 28.56%. In conclusion, the instrument developed in this study is valid and reliable, and it effectively identifies the profile and causes of students' misconceptions.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes. The web application framework must have safeguards to stop and find application vulnerabilities. Among all web application attacks, SQL injection and XSS attacks are common, which may lead to severe damage to Web application data or web functionalities. Currently, there are many solutions provided by various study for SQLi and XSS attack detection, but most of the work shown have used either SQL/XSS payload-based detection or HTTP request-based detection. Few solutions available can detect SQLi and XSS attacks, but these methods provide very high false positive rates, and the accuracy of these models can further be improved. We proposed a novel approach for securing web applications from both cross-site scripting attacks and SQL injection attacks using decoding and standardization of SQL and XSS payloads and HTTP requests and trained our model using hybrid deep learning networks in this paper. The proposed hybrid DL model combines the strengths of CNNs in extracting features from input data and LSTMs in capturing temporal dependencies in sequential data. The soundness of our approach lies in the use of deep learning techniques that can identify subtle patterns in the data that traditional machine learning-based methods might miss. We have created a testbed dataset of Normal and SQLi/XSS HTTP requests and evaluated the performance of our model on this dataset. We have also trained and evaluated the proposed model on the Benchmark dataset HTTP CSIC 2010 and another SQL/XSS payload dataset. The experimental findings show that our proposed approach effectively identifies these attacks with high accuracy and a low percentage of false positives. Additionally, our model performed better than traditional machine learning-based methods. This soundness approach can be applied to various network security applications such as intrusion detection systems and web application firewalls. Using our model, we achieved an accuracy of 99.84%, 99.23% and 99.77% on the SQL-XSS Payload dataset, Testbed dataset and HTTP CSIC 2010 dataset, respectively.

Assessment the Ability of Free/Total Ratio of Serum PSA in Diagnosing Prostate Cancer and Benign Prostatic Hyperplasia

Background: The Prostate gland is the male secondary sexual organ most commonly affected by benign or malignant neoplasm. Prostate cancer (Ca-P) is one of the most commonly diagnosed cancers in men and is now the third commonest cause of cancer death in western countries.The aim of the study was to evaluate the use of serum prostate-specific antigen levels in differentiating prostate cancer from benign prostatic hyperplasia. Methods:The present study is a cross-sectional observational study.The study was conducted at the outpatient department and indoor patients at the department of Urology, Rajshahi Medical College Hospital (RMCH), Rajshahi, and different private clinic in Rajshahi City during the period from July 2019 to June 2020.The study population included male patients above 50 years of age, who attended in the department of Urology, Rajshahi Medical College Hospital (RMCH), Rajshahi, and different private clinic in Rajshahi City complaining of irritative or obstructive lower urinary tract symptoms (LUTS) suspected as clinically prostate cancer or benign prostatic hyperplasia (BPHStatistical analysis of the results was obtained by using window-based computer software devised with Statistical Packages for Social Sciences (SPSS-24). Results: The age distribution of the prostatic carcinoma and BPH patients. Prostatic carcinoma was observed to be significantly higher from 80 years onwards, while BPH was common between 60 - 80 years (p = 0.00001). Table 10shows the validity or accuracy of the free/total ratio of serum PSA as a diagnostic test in differentiating prostatic carcinoma from BPH in patientswith total PSA levels between 4-10ng/ml. The percentage of false positives was significantly higher when total PSA ratio was used to diagnose the disease compared to when free to total PSA was used as a diagnostic tool (p&lt;0.000001). The overall accuracy of total PSA was also significantly lower (61.45%) than that of free/total PSA (91.67%) (p&lt; 0.011526). Conclusion: Free and total prostate specific antigen (PSA) is increased significantly in carcinoma prostate than benign prostatic hyperplasia but free/total ratio of serum prostate- specific antigen level significantly decreases in prostatic carcinoma than benign prostatic hyperplasia. TAJ 2023; 36: No-2: 215-230

Centre pork chop colour classification using image analysis on the ventral surface of the loin

Image analysis of ventral, anterior, and posterior ends of 550 pork loins was used to classify extreme-coloured centre chops. Only the ventral side was strongly correlated ( r = 0.79) to the centre chop. Thresholds at 2.5th, 5th, and 7.5th percentiles of ventral surface pale pork were selected to classify centre chop pale pork (5th percentile). Increasing stringency increased accuracy in excluding pale samples. At the 7.5th percentile, prediction accuracy of pale (85.7%) and non-pale samples (89.5%) was balanced. This method can be adopted by commercial pork processors as a classification or pre-sorting approach.

Thyrotropin Screening of Newborns: Before or After 72 Hours of Life? Before Discharge or at Home?

Objective: Until November 2019 in Belgium, dried blood spot (DBS) sampling was performed between 72 and 120 hours of life, when a majority of newborns had already been discharged from the maternity. In November 2019, the policy for newborn screening in South Belgium changed to allow sampling as soon as 48 hours of life, with the objective to accelerate the process and to allow more sampling during the hospital stay. Our objective was to evaluate the impact of this policy modification and, in particular, to assess the effectiveness of screening for hypothyroidism based on sampling before or after 72 hours of life, as well as to compare the effectiveness of DBS collection before discharge or at home. Methods: This retrospective study included live births ≥37 weeks of gestation, screened by the Université Libre de Bruxelles Newborn Screening Center between January 2019 and December 2021. To evaluate the efficiency of early sampling, we compared thyrotropin (TSH) results for screening <72 hours and screening ≥72 hours. We also compared TSH results of DBS performed before discharge with those performed at home. Results: A total of 53,794 newborns were included. The results of 24,816 healthy newborns screened before 72 hours of life and of 28,978 healthy newborns screened between 72 and 144 hours of life were compared. The median TSH level was similar (1.50 and 1.20 mU/L, respectively). The percentage of false positives was similar (0.08% and 0.07%, respectively). Earlier sampling, before 72 hours, allowed treatment of positive cases at 6 days rather than 8.5 days. DBS sampling at home resulted in longer delay for transferring the sample to the laboratory (a median of 3.0 days for hospital sampling vs. 5.0 days for home sampling). A poorer quality of home blood sampling was observed, with 0.27% unusable samples compared with 0.06% unusable samples for hospital sampling (p < 0.001). Conclusions: In term newborns, TSH screening before discharge, as early as 48 hours of life, is a valid strategy. It allows earlier treatment of positive cases, does not increase the percentage of false positives, and results in fewer unusable samples.

Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence.

In our previous study, we developed a combined diagnostic model based on time-intensity curve (TIC) types and radiomics signature on contrast-enhanced magnetic resonance imaging (CE-MRI) for non-mass enhancement (NME). The model had a high diagnostic ability for differentiation without the additional diffusion-weighted imaging (DWI) sequence. In this study, we aimed to compare the diagnostic performance of the combined clinical-radiomics model based on CE-MRI and DWI in discriminating Breast Imaging-Reporting and Data System (BI-RADS) 4 NME breast lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma. This retrospective study enrolled 364 NME lesions (343 patients). Of these, 183 malignant and 84 benign breast lesions classified as BI-RADS 4 NMEs by the initial diagnosis were reclassified based on the combined clinical-radiomics model and DWI, respectively. The nomogram score (NS) values for malignancy risk derived from the combined clinical-radiomics model and the minimal apparent diffusion coefficient (ADC) values from DWI were calculated and compared. The percentage of false positives were estimated in comparison with the original classification. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic value of the NS and minimal ADC values in distinguishing benign and malignant lesions, DCIS, and invasive breast carcinoma. An ablation experiment was used to test the value of the additional DWI sequence. The diagnostic value of the NS values [area under curve (AUC) =0.843; 95% CI: 0.789-0.896] for discriminating the 267 NME breast lesions categorized as BI-RADS 4 was significantly higher than the minimal ADC values (AUC =0.662; 95% CI: 0.590-0.735). The NS values showed higher sensitivity, specificity, and accuracy compared with the minimal ADC values (sensitivity: 80.3% vs. 65.6%; specificity: 79.8% vs. 65.5%; accuracy: 80.1% vs. 65.5%). The NS values and minimal ADC values did not achieve high diagnostic accuracy in discriminating between DCIS and invasive cancer. However, the diagnostic performance of the combined NS-ADC model (AUC =0.731; 95% CI: 0.655-0.806) was higher than that of the NS values alone (P=0.008) and comparable to that of the minimal ADC values (P=0.440). The combined clinical-radiomics model based on CE-MRI could improve the diagnostic performance in discriminating the BI-RADS 4 NME lesions without an additional DWI sequence. However, DWI may improve the diagnostic performance in discriminating DCIS from invasive cancer.

Optical detection of infiltration during peripheral intravenous infusion in neonates.

Infiltration and extravasation are common complications during peripheral intravenous infusion in the neonatal intensive care unit, and diagnosis is usually clinical, by inspection and palpation. Delay in diagnosis due to poor surveillance or misinterpretation of clinical signs may carry serious damage to the tissues of the neonate. Recently, a novel technology based on optical detection of infiltration has become available. We have studied two groups of term and preterm infants receiving non-vesicant intravenous infusions by the peripheral route (24G short peripheral cannulas), and we evaluated the incidence of infiltration. In the first group, we have compared the clinical detection of infiltration versus the detection obtained by a novel optical device, blinding the alarms of the device. In the second group, the comparison was carried out without blinding the alarms. Of the neonates included in this study, 60% were female, 86% had a gestational age <37 weeks (34 ± 2.5 weeks) and a mean birth weight of 2.08 ± 0.4 g. Total recorded monitoring time was 1318 h and average monitoring time for each short peripheral cannula was 26.4 h. The incidence of infiltration was 80%, most of them having a Millam score <2. The novel device showed an overall sensitivity of 88.9% in detecting infiltration. Specificity was 84.4%, as evaluated assuming as standard the clinical diagnosis. Interestingly, in cases of documented infiltration, the event was diagnosed by the optical device approximately 6 h before the clinical diagnosis. Continuous monitoring of the insertion site, as automatically ensured by this novel optical device, may play a complementary role in early detection of infiltration, even if the percentage of false positives and false negatives suggests that periodic clinical assessment by expert nurses cannot be omitted.

Utilizing flow cytometry sorting signal width to enrich for cells positive to endogenous gene integration of fluorescent proteins.

Endogenous gene knock-in using CRIPSR is becoming the standard for fluorescent tagging of endogenous proteins. Some protocols, particularly those that utilize insert cassettes that carry a fluorescent protein tag, can yield many types of cells with off-target insertions that have diffuse fluorescent signal throughout the whole cell in addition to scarce cells with on-target gene insertions that show the correct sub-cellular localization of the tagged protein. As such, when searching for cells with on-target integration using flow cytometry, the off-target fluorescent cells yield a high percentage of false positives. Here, we show that by changing the gating used to select for fluorescence during flow cytometry sorting, namely utilizing the width of the signal as opposed to the area, we can highly enrich for positively integrated cells. Reproducible gates were created to select even minuscule percentages of correct subcellular signal, and these parameters were validated by fluorescence microscopy. This method is a powerful tool to rapidly enhance the generation of cell lines with correctly integrated gene knock-ins encoding endogenous fluorescent proteins.

Diagnostic Yield of Computed Tomography for Cancer Detection in a Tertiary Referral Population of Idiopathic Inflammatory Myositis Patients.

To inform guidance for cancer detection in patients with idiopathic inflammatory myopathy (IIM), we evaluated the diagnostic yield of computed tomography (CT) imaging for cancer screening/surveillance within distinct IIM subtypes and myositis-specific autoantibody strata. We conducted a single-center, retrospective cohort study in IIM patients. Overall diagnostic yield (number of cancers diagnosed/number of tests performed), percentage of false positives (number of biopsies performed not leading to cancer diagnosis/number of tests performed), and test characteristics were determined on CT of the chest and abdomen/pelvis. Within the first 3 years since IIM symptom onset, a total of 9 of 1,011 (0.9%) chest CT scans and 12 of 657 (1.8%) abdomen/pelvis CT scans detected cancer. Diagnostic yields for both CT of the chest and CT of the abdomen/pelvis were highest in dermatomyositis, specifically anti-transcription intermediary factor 1γ (2.9% and 2.4% for CT of the chest and abdomen/pelvis, respectively). The highest percentage of false positives was in patients with antisynthetase syndrome (ASyS) (4.4%) and immune-mediated necrotizing myopathy (4.4%) on CT of the chest, and ASyS (3.8%) on CT of the abdomen/pelvis. Patients ages <40 years old at IIM onset had both low diagnostic yields (0% and 0.5%) and high false-positive rates (1.9% and 4.4%) for CT of the chest and abdomen/pelvis, respectively. In a tertiary referral cohort of IIM patients, CT imaging has a wide range of diagnostic yield and frequency of false positives for contemporaneous cancer. These findings suggest that cancer detection strategies targeted according to IIM subtype, autoantibody positivity, and age may maximize cancer detection while minimizing the harms and costs of over-screening.

Blockchain-Based Privacy Preservation Scheme for Misbehavior Detection in Lightweight IoMT Devices.

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

The Role of C- Reactive Protein (CRP) in the Diagnosis of Neonatal Sepsis

Background: Neonatal sepsis or septicemia is the term that has been used to describe the systemic response to infection in a newborn infant during the first 28 days of life. Neonatal mortality is increasingly recognized as an important global public health challenge that must be addressed if we wish to reduce child death disparities between rich and poor countries. Most of the estimated 4 million neonatal deaths per year occur in low and middle-income countries. Objective: The aim of this study is to evaluate the role of C-reactive protein (CRP) in the diagnosis of neonatal sepsis. Method of the Study: This is a cross-sectional study carried out in the Department of Neonatology, BSMMU, Dhaka over a period of 6 months between January 2013 to June 2013. Newborns with suspected sepsis admitted to BSMMU were the study population. Clinically diagnosed cases of neonatal sepsis aged &lt; 28 days of both sexes whose parents or guardians provided informed consent were eligible for enrollment in the study. The subjects were selected consecutively from the study population. Data were collected using a structured questionnaire containing all the variables of interest. Data were processed and analyzed using the computer software SPSS (Statistical Package for Social Sciences). The test statistics used to analyze the data were Chi-square (χ2) or Fisher’s Exact Probability Test &amp; Student’s t-Test. Result: The sensitivity of CRP in correctly detecting neonatal sepsis of those who have the disease is 94.44%, while the specificity of the test in correctly differentiating neonates who do not have the disease is 13.72%. The positive predictive value (PPV) of the test is 27.86% and the negative predictive value of the test is 87.5%. The percentages of false positives and false negatives as yielded by the test are 72.13% and 12.5% respectively. Conclusion: The sensitivity of CRP was higher at 94.44% but specificity was low 13.72%. A combination of tests may increase the sensitivity, ........

Aspect of COVID-19 Pandemic: Case for Mass Spectrometry

Fast spread of COVID-19 worldwide causing unprecedented number of coronavirus infections, Scientist community are in race to control the disease by the development of both novel effective diagnostics strategies and effective therapies. Ultimately an efficient vaccine. Research efforts has focused on recognition of the pathogen entry mechanisms to the host's cells, simultaneously characterize the host’s cellular defence mechanisms in response to the pathogen attack. Early genome sequencing enlighten us on the origin of the virus responsible for the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) pandemic. PCR tests first implemented to recognize the presence of viral RNA. PCR tests for SARS-CoV-2 has no gold standard reference, which limits the accuracy of results and increases the percentages of false positives, besides PCR test are unable to predict disease status (progression). Alternative techniques where tested and evaluated, such as immunological based tests. Mass spectrometric methods. Which was suggested as a complementary method to the structural information gathered by molecular level test? Here, we highlight on the contributions of mass spectrometry to several areas of SARS-CoV-2 infectious disease research understanding the host’s cell proteome changes including diagnostics techniques, vaccines and therapies.

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.

Application of YOLOX deep learning model for automated object detection on thermograms

A method of automating the data analysis of thermal imaging systems in the field of safety control is proposed. It has been established that today video surveillance technologies have a number of disadvantages that can be eliminated by using thermal imaging cameras. Analysis of infrared images can be automated in order to reduce percentage of false positives and increase the efficiency of thermal imaging video surveillance systems. A high level of interference, unclear object contours and low image resolution are real problems in automating the object detecting process on thermographic images. The traditional and promising methods of thermograms analysis and approaches that can lead to creating the automated thermal video surveillance systems are discussed. It is proposed to use deep learning, which in recent years has proven itself as an effective way of image analysis. The study is based on review of existing works, as methods of automating object detection process on thermograms. It is proposed to use YOLOX as a deep learning model. This model has one of the best quality indicators and speed processing input parameters on standard datasets. FLIR’s Thermal Starter annotated set of thermal images is used to train the model. The value of mAP at the level 55% is obtained according the results of model training for recognizing 4 classes of objects on thermograms. Different advantages and disadvantages of this development are analyzed. Ways of further improvement of the neural network method of automation of thermal imaging safety control systems have been determined.

Machine-learning algorithm in acute stroke: real-world experience

To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke. CT and CT angiography (CTA) studies of 104 consecutive patients (43 females, age range 19-93, median age 62) performed for suspected acute stroke at a single tertiary institutionwith real-time ML software analysis (RAPID™ ASPECTS and CTA) were included. Studies were retrospectively reviewed independently by two neuroradiologists in a blinded manner. The cohort included 24 acute infarcts and 16 large vessel occlusions (LVO). RAPID™ ASPECTS interpretation demonstrated high sensitivity (87.5%) and NPV (87.5%) but very poor specificity (30.9%) and PPV (30.9%) for detection of acute ischaemic parenchymal changes. There was a high percentage of false positives (51.1%).In cases of proven LVO, RAPID™ ASPECTS showed good correlation with neuroradiologists' blinded independent interpretation, Pearson correlation coefficient = 0.96 (both readers), 0.63 (RAPID™ vs reader 1), 0.69 (RAPID™ vs reader 2). RAPID™ CTA interpretation demonstrated high sensitivity (92.3%), specificity (85.3%), and negative predictive (NPV) (98.5%) with moderate positive predictive value (PPV) (52.2%) for detection of LVO (N=13). False positives accounted for 12.5% of cases, of which 27.3% were attributed to arterial stenosis. RAPID™ CTA was robust and reliable in detection of LVO. Although demonstrating high sensitivity and NPV, RAPID™ ASPECTS interpretation was associated with a high number of false positives, which decreased clinicians' confidence in the algorithm. However, in cases of proven LVO, RAPID™ ASPECTS performed well and had good correlation with neuroradiologists' blinded interpretation.

Use of injectable progesterone at the beginning of the TAI protocol is not necessary in super-early resynchronization started 14 days after artificial insemination in Bos indicus beef heifers

In a protocol for the resynchronization of ovulation starting 14 days (D14) after timed artificial insemination, the effects of short-action injectable progesterone (P4i) administration and the length of treatment with an intravaginal progesterone (P4) device on follicular dynamics, the conception rate (P/AI) and the percentage of false positives were evaluated in 1065 Nelore heifers previously timed-inseminated. On D14, P4 devices were inserted, and heifers allocated, based on a 2 × 2 factorial design, to receive (P4i) or not 50 mg of P4i (NoP4i), and to remove the P4 device after 6 (6Day) or 8 days (8Day). At the time of P4 device removal (D20 and D22), non-pregnancy diagnosis was performed using vascularization of the corpus luteum (VCL) evaluated. At this time, non-pregnant heifers received 150 μg of D-cloprostenol, 0.6 mg of estradiol cypionate and 300IU of eCG. TAI was performed 48 h after P4 device removal. For these heifers, ultrasound examinations were performed at P4 device removal and at TAI to evaluate follicular dynamics and at 30 days after TAI to evaluate the P/AI. Pregnant heifers based on VCL were evaluated using B-mode ultrasonography 10 days after Doppler ultrasound to evaluate the percentage of false positives. Statistical analyses were performed using the GLIMMIX procedure of SAS. There were no interaction effects between P4i and duration of treatment with a P4 device. The P/AI was diagnosed by Doppler, 1st TAI, 2nd TAI, total and percentage of false positives did not differ between heifers receiving or not P4i. Similarly, duration of treatment with a P4 device did not influence the P/AI by Doppler, 1st TAI, 2nd TAI and total. However, the percentage of false positives diagnoses was higher in 6Day heifers (P = 0.01). The diameter of largest follicle at P4 device removal was greater in the 8Day heifers (P = 0.001), and at TAI was higher in the P4i-treated heifers (P = 0.03). Additionally, the percentage of false positives diagnoses was lower in heifers that ovulated to the 1st TAI protocol (P = 0.001). In conclusion, for resynchronization 14 days after TAI, it is not necessary to inject P4i at the beginning of the protocol. In addition, P4 device removal after 6 instead of 8 days increases the percentage of false positives because of the earlier diagnosis (20 days after TAI), but does not interfere in P/AI of resynchronization protocol. Furthermore, the percentage of false positives is higher in heifers that did not ovulate to 1st TAI protocol.

Positive predictive value of ultrasound in correctly identifying an inguinal hernia: a single-centered retrospective pilot study

ObjectivesTo determine the clinical utility of preoperative ultrasound imaging for predicting an inguinal hernia in need of surgery. In addition, we aimed to identify factors associated with false positive (FP) ultrasound examinations.MethodsIn this retrospective pilot study, we included all 175 patients who underwent inguinal hernia surgery in our hospital in 2019 and of whom a positive preoperative ultrasound examination of the groin area was available. The positive predictive value (PPV) of the ultrasound examination was determined using inguinal hernia detected during surgery (yes/no) as golden standard. To identify possible predictive factors, we compared the characteristics of patients with a FP ultrasound with patients with a true positive (TP) ultrasound.ResultsPPV of ultrasound examinations to identify an inguinal hernia in need of surgery correctly was 90.9% (159/175). The patients with a FP ultrasound examination had a significantly higher body mass index (BMI) than the patients with a TP ultrasound examination (27.6 ± 4.2 vs 25.8 ± 2.3, p = 0.043).ConclusionsWith a false positive percentage of 9.1%, there is still room for improvement of preoperative diagnostic imaging. Studies with larger cohorts are necessary to establish prediction models that have the potential to reduce FP ultrasound results.