Full Review Research Articles

Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan.

Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.

Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges.

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Balint Orban Memorial Program Abstracts.

Journal of PeriodontologyAccepted Articles MEETING ABSTRACTS Balint Orban Memorial Program Abstracts First published: 23 May 2023 https://doi.org/10.1002/JPER.23-0287 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as https://doi.org/10.1002/JPER.23-0287 AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Accepted ArticlesAccepted, unedited articles published online and citable. The final edited and typeset version of record will appear in the future. RelatedInformation

The Impact of Social Vulnerability on Substance Use Detection Practices in Pregnancy.

This study aimed to synthesize current literature regarding the impact of social vulnerability on pregnancy-related substance use detection in the United States and highlight disparities in substance use detection practices. Clinicaltrials.gov, Google Scholar, PubMed (includes MEDLINE), and Cochrane Library databases were searched using the following Medical Subject Headings (MeSH): (["pregnancy" or "prenatal"] AND ["substance use screening" or "urine toxicology testing" or "toxicology testing" or "urine drug screening" or "CRAFFT" or "4P's" or "4P's Plus" or "NIDA Quick Screen" or "DAST-10" or "SURP-P" or "WIDUS"], AND ("bias" or "disparities" or "social vulnerability"]). The search included systematic reviews, prospective and retrospective studies, randomized controlled trials, case studies, and qualitative and quantitative research from January 2014 through November 2023. Selected literature was limited to studies published in English, which included a study population of either pregnant individuals or pregnancy health care providers in the United States, and that were focused on inequities in pregnancy substance use detection. Using Covidence, three authors screened abstracts, and two screened full articles for inclusion. The included studies were evaluated for quality of evidence using the mixed methods appraisal tool. The search yielded 4,188 manuscripts; 37 were eligible for full review. A total of 18 manuscripts were included based on the relevancy of the topic. The most common social vulnerability domain identified was minority status (17/18), followed by socioeconomic status (11/18), household characteristics (8/18), and housing type (1/18). Social vulnerability plays a role in substance use detection among pregnant individuals. Most notably, race and ethnicity, age, and public insurance lead to increased rates of detection, though most individual factors need to be studied in greater depth. This study was registered with PROSPERO (PROSPERO ID CRD42022352598), the International Prospective Register of Systematic Reviews. KEY POINTS: · Socially vulnerable pregnant individuals are more likely to receive substance use screening or urine toxicology testing.. · Race, ethnicity, age, and insurance influence substance use detection disparities.. · More research is needed to understand how other characteristics influence disparities in substance use detection..

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.

Purpose To evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variations in image appearance. Materials and Methods In December 2021, the DL bone age model that won the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated using the RSNA validation set (1425 pediatric hand radiographs; internal test set in this study) and the Digital Hand Atlas (DHA) (1202 pediatric hand radiographs; external test set). Each test image underwent seven types of transformations (rotations, flips, brightness, contrast, inversion, laterality marker, and resolution) to represent a range of image appearances, many of which simulate real-world variations. Computational "stress tests" were performed by comparing the model's predictions on baseline and transformed images. Mean absolute differences (MADs) of predicted bone ages compared with radiologist-determined ground truth on baseline versus transformed images were compared using Wilcoxon signed rank tests. The proportion of clinically significant errors (CSEs) was compared using McNemar tests. Results There was no evidence of a difference in MAD of the model on the two baseline test sets (RSNA = 6.8 months, DHA = 6.9 months; P = .05), indicating good model generalization to external data. Except for the RSNA dataset images with an appended radiologic laterality marker (P = .86), there were significant differences in MAD for both the DHA and RSNA datasets among other transformation groups (rotations, flips, brightness, contrast, inversion, and resolution). There were significant differences in proportion of CSEs for 57% of the image transformations (19 of 33) performed on the DHA dataset. Conclusion Although an award-winning pediatric bone age DL model generalized well to curated external images, it had inconsistent predictions on images that had undergone simple transformations reflective of several real-world variations in image appearance. Keywords: Pediatrics, Hand, Convolutional Neural Network, Radiography Supplemental material is available for this article. © RSNA, 2024 See also commentary by Faghani and Erickson in this issue.

Characteristics of Patients With Initial Clostridioides difficile Infection (CDI) That Are Associated With Increased Risk of Multiple CDI Recurrences.

Because interventions are available to prevent further recurrence in patients with recurrent Clostridioides difficile infection (rCDI), we identified predictors of multiple rCDI (mrCDI) in adults at the time of presentation with initial CDI (iCDI). iCDI was defined as a positive C difficile test in any clinical setting during January 2018-August 2019 in a person aged ≥18 years with no known prior positive test. rCDI was defined as a positive test ≥14 days from the previous positive test within 180 days after iCDI; mrCDI was defined as ≥2 rCDI. We performed multivariable logistic regression analysis. Of 18 829 patients with iCDI, 882 (4.7%) had mrCDI; 437 with mrCDI and 7484 without mrCDI had full chart reviews. A higher proportion of patients with mrCDI than without mrCDI were aged ≥65 years (57.2% vs 40.7%; P < .0001) and had healthcare (59.1% vs 46.9%; P < .0001) and antibiotic (77.3% vs 67.3%; P < .0001) exposures in the 12 weeks preceding iCDI. In multivariable analysis, age ≥65 years (adjusted odds ratio [aOR], 1.91; 95% confidence interval [CI], 1.55-2.35), chronic hemodialysis (aOR, 2.28; 95% CI, 1.48-3.51), hospitalization (aOR, 1.64; 95% CI, 1.33-2.01), and nitrofurantoin use (aOR, 1.95; 95% CI, 1.18-3.23) in the 12 weeks preceding iCDI were associated with mrCDI. Patients with iCDI who are older, on hemodialysis, or had recent hospitalization or nitrofurantoin use had increased risk of mrCDI and may benefit from early use of adjunctive therapy to prevent mrCDI. If confirmed, these findings could aid in clinical decision making and interventional study designs.

Faktor- Faktor Yang Mempengaruhi Kejadian Stunting Pada Balita Di Indonesia Tahun 2024

Stunting caused by malnutrition during the growth and development process has a higher risk of illness and death in adulthood, so it can hamper motor skills and reduce the level of productivity and competitiveness of a nation (Rahmadhita, 2020). National nutritional status survey data (SSGI) in 2022, the prevalence of stunting was 21.6%. The target stunting rate in Indonesia is 14% in 2024 and the WHO standard target is below 20%, therefore how important it is to handle stunting in Indonesia. The aim of this research is to determine the factors that influence the incidence of stunting in toddlers. The method in this research is a full text critical review in Indonesian. Articles were selected in stages using the appraisal tool preferred reporting items for systematic reviews and meta analyzes (PRISMA), then duplicate filtering was carried out, screening was carried out based on title and abstract, full text, feasibility studies and until the final stage 11 articles were found that met the inclusion and study criteria. appropriateness. The results of this research show that the factors that influence the incidence of stunting in toddlers are exclusive breastfeeding, low birth weight, infectious diseases, mother's height, mother's education, family income, and nutritional status of pregnant women.

(178) Climacturia: Emerging Type of Urinary Incontinence, Its Prevalence, Risk Factors, and Management: A Systematic Review and Prevalence Meta-analysis

Abstract Introduction Climacturia is an emerging type of urinary incontinence which occurs immediately following orgasm. Both men and women may suffer from climacturia in equivocal rates. This phenomenon is becoming more problematic as it impacts physiological and psychosexual aspect of well-being and quality of life. While awareness of climacturia becomes evident, studies are inadequate to support evidenced-based management in primary and urologic care settings. Objective This study aimed to conduct a systematic review and prevalence meta-analysis of climacturia and investigate its risk factors and evidenced-based management. Methods A systematic review of 24 articles were conducted following PRISMA 2020 guidelines. The PubMed, CINAHL, and Cochrane Library database were searched using the word “climacturia” and phrase “orgasm associated urinary incontinence” respectively. Search limiters applied included literature written in English from year of inception to 2022. Title and abstract reviews were conducted on 78 articles. Articles which did not discuss climacturia, duplicates, and previous systematic reviews were excluded after full article review. Results The review accounts a total of N = 7798 subjects which are composed of N = 7629 (97.83%) males and N = 169 (2.17%) females with age range of 18 to 80 years old. A total of N = 2, 039 (26%) participants are with climacturia, N = 87 or 4.26% are females and N = 1, 931 or 95.74% are males. The risk factors associated with climacturia include sexually active male, history of prostate cancer after prostatectomy within 12 months of surgery or radiation therapy, increased Body Mass Index (BMI), erectile dysfunction (ED), increased urethral width, presence of urinary incontinence, pain-associated orgasm and loss of penile length after pelvic surgery, use of aides to obtain an erection, and increased incontinence scores for men. For women, the risk factors are high scores of female sexual function index (FSFI) and detrusor overactivity. The management for females is limited to pharmacologic therapy with use of antimuscarinic agents. While, behavioral therapy, pharmacologic regimen, pelvic floor muscle training (PFMT), surgery (sling procedure) are the management options for affected men. Conclusions Climacturia affects males more than females, however, the evidence to support the efficacy of treatment options for both sexes are equally inadequate. Primary care and Advanced Practice Providers in primary and urologic care settings are at best position to query patients for the presence of and manage climacturia in timely manner. Further research is needed to increase evidence supporting effective management of climacturia for both men and women. Disclosure No.

Denoising Multiphase Functional Cardiac CT Angiography Using Deep Learning and Synthetic Data.

Coronary CT angiography is increasingly used for cardiac diagnosis. Dose modulation techniques can reduce radiation dose, but resulting functional images are noisy and challenging for functional analysis. This retrospective study describes and evaluates a deep learning method for denoising functional cardiac imaging, taking advantage of multiphase information in a three-dimensional convolutional neural network. Coronary CT angiograms (n = 566) were used to derive synthetic data for training. Deep learning-based image denoising was compared with unprocessed images and a standard noise reduction algorithm (block-matching and three-dimensional filtering [BM3D]). Noise and signal-to-noise ratio measurements, as well as expert evaluation of image quality, were performed. To validate the use of the denoised images for cardiac quantification, threshold-based segmentation was performed, and results were compared with manual measurements on unprocessed images. Deep learning-based denoised images showed significantly improved noise compared with standard denoising-based images (SD of left ventricular blood pool, 20.3 HU ± 42.5 [SD] vs 33.4 HU ± 39.8 for deep learning-based image denoising vs BM3D; P < .0001). Expert evaluations of image quality were significantly higher in deep learning-based denoised images compared with standard denoising. Semiautomatic left ventricular size measurements on deep learning-based denoised images showed excellent correlation with expert quantification on unprocessed images (intraclass correlation coefficient, 0.97). Deep learning-based denoising using a three-dimensional approach resulted in excellent denoising performance and facilitated valid automatic processing of cardiac functional imaging. Keywords: Cardiac CT Angiography, Deep Learning, Image Denoising Supplemental material is available for this article. © RSNA, 2024.

A scoping review and narrative synthesis of neonatal skin injury severity scales.

Mechanical force skin injuries are common for critical care patients, especially neonates. Currently, identification and severity assessments of injuries are dependent on clinical experience and/or utilization of severity tools. Compared with adults, neonates sustain skin injuries in different anatomical locations and have decreased layers of healthy tissue (from 0.9 to 1.2 mm) creating questions around direct application of adult injury severity scales reliant on visual assessment. The aim of this scoping review (ScR) was to investigate severity scales used to report hospital acquired skin injuries for neonates. This study utilized the 2015 Joanna Briggs Institute methodology for scoping reviews and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews extension. PubMed, CINAHL, COCHRANE Central, Scopus, and the reference lists of included studies were searched for studies published between 2001 and 2023, that included severity scales use within neonatal population. Two authors independently identified studies for full review, data extraction, and quality assessment. A systematic database search returned 1163 records. After full test review of 109 studies, 35 studies were included. A majority of studies included were cohort or action research and conducted in the United States of America. Most studies (57%, n = 20) reported skin injuries acquired throughout the body, 14 (40%) of the studies reported the nasal area alone and one study reported no anatomical location. A total of nine severity scales or combination of scales were utilized within studies (n = 31) and four studies did not report a scale. Various versions of scales from the National Pressure Ulcer Advisory Panel (n = 16), European Pressure Ulcer Advisory Panel (n = 8) or Neonatal Skin Condition Score (n = 4) were reported, compared with locally developed classifications/scales (n = 4). Scales were predominantly of ordinal grouping (74%, n = 26) or categorical assessment (14%, n = 5). Only one scale from 2004 was validated for neonates. Neonatal skin injuries will continue to be reported subjectively until severity scales are consistently applied or other measurements are identified to support assessment. Additionally, without skin injury assessment uniformity, critical examination of effectiveness of skin care treatment practices will have subjective comparison. This review suggests there is a need for consistent skin assessment and severity scales that are valid for the neonatal population and their unique skin considerations.

Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists.

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.

Counseling in fetal medicine: update on mild and moderate fetal ventriculomegaly.

Ultrasound in Obstetrics & GynecologyAccepted Articles Opinion Counseling in fetal medicine: update on mild and moderate fetal ventriculomegaly D. Di Mascio, D. Di Mascio orcid.org/0000-0002-6560-3393 Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Rome, ItalySearch for more papers by this authorF. D'Antonio, Corresponding Author F. D'Antonio [email protected] orcid.org/0000-0002-7546-8025 Center for Fetal Care and High-Risk Pregnancy, Department of Obstetrics and Gynecology, University of Chieti, Chieti, ItalyCorrespondence to: Prof. F. D'Antonio, Center for Fetal Care and High-Risk Pregnancy, Department of Obstetrics and Gynecology, University of Chieti, Chieti, Italy, E-mail: [email protected]Search for more papers by this authorG. Rizzo, G. Rizzo orcid.org/0000-0002-5525-4353 Department of Obstetrics and Gynecology, Fondazione Policlinico Tor Vergata, University of Rome Tor Vergata, Rome, ItalySearch for more papers by this authorG. Pilu, G. Pilu Obstetric Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, ItalySearch for more papers by this authorA. Khalil, A. Khalil orcid.org/0000-0003-2802-7670 Fetal Medicine Unit, St George's Hospital, London, UK Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK Fetal Medicine Unit, Liverpool Women's Hospital, University of Liverpool, Liverpool, UKSearch for more papers by this authorA. Papageorghiou, A. Papageorghiou Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UKSearch for more papers by this author D. Di Mascio, D. Di Mascio orcid.org/0000-0002-6560-3393 Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Rome, ItalySearch for more papers by this authorF. D'Antonio, Corresponding Author F. D'Antonio [email protected] orcid.org/0000-0002-7546-8025 Center for Fetal Care and High-Risk Pregnancy, Department of Obstetrics and Gynecology, University of Chieti, Chieti, ItalyCorrespondence to: Prof. F. D'Antonio, Center for Fetal Care and High-Risk Pregnancy, Department of Obstetrics and Gynecology, University of Chieti, Chieti, Italy, E-mail: [email protected]Search for more papers by this authorG. Rizzo, G. Rizzo orcid.org/0000-0002-5525-4353 Department of Obstetrics and Gynecology, Fondazione Policlinico Tor Vergata, University of Rome Tor Vergata, Rome, ItalySearch for more papers by this authorG. Pilu, G. Pilu Obstetric Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, ItalySearch for more papers by this authorA. Khalil, A. Khalil orcid.org/0000-0003-2802-7670 Fetal Medicine Unit, St George's Hospital, London, UK Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK Fetal Medicine Unit, Liverpool Women's Hospital, University of Liverpool, Liverpool, UKSearch for more papers by this authorA. Papageorghiou, A. Papageorghiou Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UKSearch for more papers by this author First published: 19 May 2023 https://doi.org/10.1002/uog.26251 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/uog.26251. AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Accepted ArticlesAccepted, unedited articles published online and citable. The final edited and typeset version of record will appear in the future. RelatedInformation

Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots

Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful. Methods: In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques. Results: Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance. Conclusion: It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.

Integrating evidence and causal mapping of factors that influence medication decision-making by pregnant women at risk of hypertensive disorder: protocol for a scoping review

IntroductionIn 2018, the American College of Obstetricians and Gynecologists recommended low-dose aspirin to prevent the onset of pre-eclampsia among women who were at high risk. Factors influencing women’s acceptance of...

Análise bibliométrica e multicritério para seleção de aditivos potencializadores de compostagem de biorresíduos municipais

ABSTRACT Municipal biowaste has a high potential for agricultural use. However, it requires an additional amendment to increase the nutritional content used during the composting process. The purpose of this study was to investigate the selection of compost-enhancing amendments from municipal biowaste using bibliometric and multicriteria analysis. The municipality of Yopal (Casanare, Colombia) was chosen as a case study. Using a search equation in the Scopus and Web of Science databases over five years (2018-2022), 53 publications were discovered, from which 25 were chosen for a full review to identify alternatives for amendment. Ashes, rice husks, sawdust, sugarcane bagasse, tree pruning waste, and alfalfa hay were assessed as amendment substitutes. Criteria such as financial cost, accessibility to acquire the amendment, nitrogen, phosphorus, and carbon contribution were used in the analysis of hierarchical processes. Tree pruning waste was the best option based on the criteria chosen because it is easily accessible and contributes with carbon and nitrogen (C/N: 13) to composting. Factors such as cost and affordability influence the physical and chemical characteristics that an amendment brings to the composting of municipal biowaste.

Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.

Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Sagreiya in this issue.

Toxicology and Metabolic Effects of Methanol and Formaldehyde on the Brain, a ReviewA rticle

Background: Methanol is a toxic alcohol for the human body. The molecular biology of methanol metabolites affecting different organs, such as the brain, is under investigation. This systematic review aimed to consider methanol toxic molecular biology, based on the original articles obtained from data banks to figure out recent achievements. Methods: Scientific articles regarding the toxic effects and metabolites of methanol on the central nervous system (CNS) were collected from valid databases and classified based on their validity. Exclusion criteria were articles with duplicates, no available full text, review articles, case reports, and letters. Results: Current metabolic reactions were addressed in the development of CNS diseases, such as optic neuropathy, basal ganglia lesions, and Alzheimer’s disease. However, proteomic investigations introduced new metabolic changes, and serum proteins regarding blood coagulation, vitamin A metabolism, and immune responses were suggested for early detection of toxicity. Conclusion: Besides CNS disorders introduced for methanol toxicity, there is no exact proteomic serum marker to diagnose toxicity soon; however, the interleukin-1 beta system is suggested as a candidate, and more investigation is required to improve its competency.

Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.

This study evaluated the ability of generative large language models (LLMs) to detect speech recognition errors in radiology reports. A dataset of 3233 CT and MRI reports was assessed by radiologists for speech recognition errors. Errors were categorized as clinically significant or not clinically significant. Performances of five generative LLMs-GPT-3.5-turbo, GPT-4, text-davinci-003, Llama-v2-70B-chat, and Bard-were compared in detecting these errors, using manual error detection as the reference standard. Prompt engineering was used to optimize model performance. GPT-4 demonstrated high accuracy in detecting clinically significant errors (precision, 76.9%; recall, 100%; F1 score, 86.9%) and not clinically significant errors (precision, 93.9%; recall, 94.7%; F1 score, 94.3%). Text-davinci-003 achieved F1 scores of 72% and 46.6% for clinically significant and not clinically significant errors, respectively. GPT-3.5-turbo obtained 59.1% and 32.2% F1 scores, while Llama-v2-70B-chat scored 72.8% and 47.7%. Bard showed the lowest accuracy, with F1 scores of 47.5% and 20.9%. GPT-4 effectively identified challenging errors of nonsense phrases and internally inconsistent statements. Longer reports, resident dictation, and overnight shifts were associated with higher error rates. In conclusion, advanced generative LLMs show potential for automatic detection of speech recognition errors in radiology reports. Keywords: CT, Large Language Model, Machine Learning, MRI, Natural Language Processing, Radiology Reports, Speech, Unsupervised Learning Supplemental material is available for this article.