Articles tagged with False Positives
The American Cancer Society has endorsed two new types of stool tests and recommends limiting the use of blood tests for colorectal cancer screening. The guidelines aim to encourage people to get screened while improving detection rates.
A new imaging test, PSMA PET/CT scan, has been shown to safely reduce the number of biopsies needed for suspected prostate cancer, with no harm to patients. The PRIMARY2 trial found that the scan could identify low-risk patients who did not need a biopsy, while targeting suspicious areas for those who did.
Researchers developed a new device that can differentiate between active HIV infection and false positives caused by vaccines, with accuracy rates of 95% and 98%. This technology has the potential to accelerate vaccine development and improve diagnosis, addressing the limitations of existing diagnostics.
A new study has mapped thousands of proteins in human blood to reveal unique molecular fingerprints for each individual. This atlas will enable blood tests to distinguish troubling signs from common ones, and provide a baseline for comparison that healthcare providers can use to flag early deviations.
A new study from the University of Missouri highlights how 'smoothed' graphs can help doctors better manage patients with hypertension. The findings suggest that these graphs can reduce errors in assessing blood pressure control, potentially alleviating pressure on the healthcare system.
A novel method generates binary class labels in highly imbalanced datasets, addressing the challenge of labeling severely imbalanced data. The approach outperformed traditional methods, providing a more efficient way to identify fraud while minimizing false positives.
A new tool called EpiScalp uses algorithms trained on dynamic network models to map brainwave patterns and identify hidden signs of epilepsy from a single routine EEG. This tool has ruled out 96% of false positives, cutting potential misdiagnoses among cases by nearly 70%, according to a Johns Hopkins University study.
A recent study found that a genetic test for opioid use disorder (OUD) had high rates of both false positive and false negative results, questioning its usefulness. The testing could lead to patients receiving opioids despite low risk or being denied effective pain relief due to high risk.
A study found that single laboratory-based HIV viral load tests used by U.S. clinicians in long-acting PrEP patients often resulted in false positives, but confirmatory testing improved accuracy.
A study of nearly 5,000 mammograms found that AI algorithms produced false positive results more frequently in Black and older patients, while Asian patients and younger patients were less likely to be flagged as suspicious. Healthcare institutions should consider the patient population they serve before purchasing an AI algorithm.
Researchers at CU Anschutz Medical Campus identified key elements to improve diagnosis and treatment of coronary heart disease (CHD) in women, addressing the common misdiagnosis of heart attacks. The new guidelines provide recommendations to help clinicians better address sex-specific symptoms and treatments.
Researchers developed an AI algorithm to identify normal mammograms and ran a simulation on patient data, revealing that AI can reduce unnecessary testing while maintaining cancer detection rates. The study suggests that AI can help doctors focus on more questionable scans, reducing false positives and improving workflows.
Researchers developed a new triplex real-time PCR test to detect the emerging Powassan virus in deer ticks, improving sensitivity and accuracy. The method reduced false negative results, providing assurance that negative test results are true negatives.
A new statistical model developed by UChicago researchers accurately identifies causal genes and variants for a disease. The tool reduces false positives and takes into account multiple genes and variants, leading to the discovery of 35 putative causal genes for LDL cholesterol levels.
A study found that the common drug Entresto (sacubitril/valsartan) increases plasma Aβ42 and Aβ40 levels in patients with cardiac failure, making them falsely positive on a debated Alzheimer's blood test. This could lead to misclassification of patients as having Alzheimer's disease.
A novel prescription wristwatch uses photoplethysmography to detect atrial fibrillation with high accuracy, even in participants with darker skin tones. The Verily Study Watch bridges the gap between long-term monitoring and consumer devices, enabling clinicians to effectively use wearable data for Afib management.
Researchers found a significant long-term increased risk of breast cancer among women with false-positive mammography results. The risk is highest in women aged 60-75 and those with low breast density, facing a 60% increased risk over the subsequent 20 years.
Researchers found that polygenic risk scores identify only 11% of individuals who develop disease and generate 5% false positives. The scores contribute little health benefit while adding cost and complexity to healthcare systems.
A new blood-based test called p-tau217 has shown great promise in identifying Alzheimer's disease by stratifying patients into low, intermediate, and high-risk groups. The two-step workflow reduces the need for confirmatory testing in uncertain cases.
Researchers developed an AI algorithm that allows electronic devices to express uncertainty when faced with unexpected data, improving cough detection technology. The new approach enables more precise detection with fewer sound samples per second, reducing computing power and addressing privacy concerns.
A large study of over 1 million women found DBT had a higher breast cancer detection rate and lower false positive rates compared to 2D digital mammography. The results suggest that DBT may be the more effective screening method for early breast cancer detection.
A novel imaging agent, 68Ga-FAPI, has been shown to reduce the number of false-positive PET/CT findings among cancer patients recently vaccinated for COVID-19. This can help prevent costly follow-up and false management decisions for cancer patients.
Researchers discovered that plasmids can linger in the nose of lab workers for weeks, interfering with clinical diagnostic tests. The study highlights the importance of considering occupational exposure in diagnosis and treatment.
A study published in the Canadian Journal of Cardiology found that smartwatch health apps detecting atrial fibrillation generated a high rate of false positives and inconclusive results, especially in patients with certain cardiac conditions. Better algorithms and machine learning may help improve the accuracy of these devices.
Researchers found a flaw in Google/Apple's exposure notification framework that can be exploited for replay attacks, potentially causing false positive notifications and disrupting trust in the system. A patch, called GAEN+, has been developed to address this issue using coarse location data from Wi-Fi access points and cell phone towers.
A retrospective study found an AI algorithm for detecting incidental pulmonary embolus (iPE) on contrast-enhanced chest CT examinations had high NPV and moderate PPV, even identifying iPEs missed by radiologists. The tool's diagnostic performance did not show significant variation across study subgroups.
A heat-loving bacterium's Cas13 protein enables specific detection of SARS-CoV-2 and other viruses in a one-pot assay. The technology has been patented and clinically validated, with the aim of mass production and commercialization.
Inadequate PPE management in Belgian testing centres caused major cross-contamination of samples, resulting in a high rate of false positive results. Scientists found that lack of glove changes between patients was the primary source of contamination.
A study published in JAMA Network Open found that half of all women will experience at least one false positive mammogram over a decade of annual breast cancer screening with 3D mammography. The risk of false positives is lower for women screened every other year, and non-dense breasts also show a lower false positive risk.
A new study developed an algorithm estimating a person's risk of developing prostate cancer based on age and two prostate cancer markers. The approach was found to reduce the number of false positives by three quarters compared to a standard PSA test while catching the same proportion of cancers, making screening safer and more accurate.
A new study co-authored by Indiana University professor M. Daniel Beneish finds that the M-Score model is still the most economically viable means of predicting corporate fraud. The model's success rate has been doubled, but at a higher cost of false positives, making it less practical for auditors to use in practice.
A Canadian study published in JAMA found a very low incidence of false-positive results among rapid antigen tests used to serially screen asymptomatic workers. The study showed only 0.05% of over 900,000 tests administered had false-positive results.
The CDC found flaws in the assay design and contamination of one component, leading to false positive reactivity in negative controls. A redesign and improved quality control measures have since been implemented.
Researchers developed an algorithm to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. The study found that 75% of plastic bag pop sounds were misclassified as gunshot sounds, highlighting the need for a diverse dataset of similar sounds.
A new protocol has been developed to identify potential false-positive COVID-19 results, particularly in asymptomatic non-exposed individuals. The method involved retesting all positive results from such patients, revealing 20 out of 288 initial positives to be false positives.
Researchers found that omitting or altering the buffer solution can generate false positive results, highlighting its essential role in test function. The study's findings have far-reaching implications for public health worldwide, as the rapid antigen test has been distributed to over 120 countries.
A new study improves AI diagnoses by penalizing algorithms for false negatives, which can be more urgent than accuracy. Researchers achieved significant improvements in precision and recall for chronic kidney disease and other conditions using cost sensitivity techniques.
Researchers developed a combined approach to amplify the large-scale biosynthetic gene cluster, resulting in a 9.59-fold increase in bleomycin production. The method uses a ZouA-dependent DNA amplification system and double-reporters-guided recombinant selection.
Researchers developed an algorithm that leverages medical informatics to predict autism spectrum disorder (ASD) diagnoses in young children. The new approach uses diagnostic codes from past doctor's visits to calculate a risk score, identifying which patients are at risk of receiving a confirmed ASD diagnosis.
A new AI system developed by NYU and NYU Abu Dhabi researchers achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. The system helps decrease false-positive findings and requested biopsies while maintaining sensitivity.
A recent study published in JNCCN found that four out of five cancer therapies tested in Phase III trials did not achieve clinically-meaningful benefits in prolonging survival. The researchers analyzed 362 industry-sponsored trials and found that 87% were either false-positive or true-negative for meeting overall survival goals.
Researchers review advancements in biosensing technologies for neonatal sepsis diagnosis, highlighting the need for faster and more accurate methods. Biosensors offer a promising solution, detecting multiple parameters simultaneously with high sensitivity and accuracy.
A recent study using REST-meta-PD analyzed brain activity data from 15 PD patient studies, finding that up to 80% of voxels declared significant may not be replicable across datasets. The researchers propose a normative modelling method to improve reproducibility and generalizability.
A new project seeks to fill a gap in identifying vulnerabilities in ML frameworks by using Software Composition Analysis and Directed Grammar-Based Fuzzing. The tool can automatically report vulnerabilities in open-source libraries that an application depends on, helping developers address them before they can be exploited.
A study assesses the reliability of a forensic identification technique using photographic pattern analysis. The results show that even at low false positive rates, true positive rates are less than 40%, suggesting extreme caution when using this technique.
A Columbia professor has developed a machine-learning based detector that automatically detects and stops lateral phishing attacks within organizations. The detector uses features like anomaly detection in communication patterns to flag suspicious emails with high precision and low false positive rates.
A University of Montana undergraduate student's research published in Ecological Applications found that a double-observer method significantly reduces false-positive errors in wildlife surveys. The study also highlighted the importance of proper training and species-specific characteristics in minimizing misidentifications.
Direct-to-consumer genetic tests may report false positive and false negative results, and interpretation is complex and context-dependent. Experts advise doctors to put results in context for patients, discussing possible sources of error and lifestyle changes.
A new MIT model has been developed to reduce false positives in credit card fraud detection, achieving a 54% reduction in incorrect flagging of legitimate transactions. By extracting more than 200 detailed features for each individual transaction, the model can better pinpoint unusual spending habits and improve accuracy.
Researchers have devised a highly sensitive method to test the chirality of materials, overcoming false positives from competing effects. By using twisted meta-molecules, they separated chirality from sources of error, allowing for accurate measurement and potential applications in fields like telecommunications and pharmaceuticals.
A team of researchers from Mayo Clinic identified a technology to address false positives in CT-based lung cancer screening, analyzing CT images and identifying disease characteristics. The findings show that a set of 57 variables can distinguish benign nodules from cancerous ones, reducing patient anxiety and unnecessary testing.
A study found that full integration of digital breast tomosynthesis (DBT) significantly reduced false positive rates in breast cancer screening exams. DBT was also associated with a higher cancer-detection rate compared to traditional digital mammography.
A new approach by NIST enables numerical testimony for ballistic comparisons, estimating the probability of false positive matches. The method produces a numerical score describing similarity between cartridge cases and estimates the probability of random effects causing a false positive match.
Researchers have designed a new assay that uses gold nanoparticles to improve the accuracy of medical screening, reducing false positives and wait times. The technology has been shown to be up to clinical standards, allowing patients to receive results in about an hour.
A new study found that women who have a false positive result from a screening mammogram are more likely to delay or forgo their subsequent screening mammogram. This delay can increase the risk of late-stage breast cancer diagnosis, which can lead to poorer treatment outcomes.
The American Cancer Society's new guidelines modify mammography ages and emphasize patient involvement in decision-making. The shift aims to balance cancer detection with the high rate of false positives, which are approximately 19 times more likely than true positives.
Neurologist Brian Callaghan's team identified 74 unnecessary medical services in neurological care, including imaging for low back pain and headaches. The goal is to inspire discussions between clinicians and patients about avoiding wasteful or unnecessary tests and treatments.
A blood test between 10-14 weeks of pregnancy accurately diagnosed all 38 fetuses with Down syndrome, surpassing standard non-invasive screening techniques. The cell-free DNA blood test correctly identified fetuses with two other less common chromosomal abnormalities.
The study found a significant decline in early recall rates from 20% to 3-4% in subsequent rounds, while lung cancer detection rates decreased by 50%. A well-organized screening program with access to previous images and results is crucial to manage false positives.
Using Lung Imaging Reporting and Data System (Lung-RADS) criteria may reduce false positives in low-dose CT lung screenings by raising the nodule size cutoff. However, this comes at the cost of reduced sensitivity.