Applications of Advanced Ultrasound Technology in Obstetrics.
Ontology highlight
ABSTRACT:
SUBMITTER: Leung KY
PROVIDER: S-EPMC8306830 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
Applications of Advanced Ultrasound Technology in Obstetrics.
Diagnostics (Basel, Switzerland) 20210706 7
Over the years, there have been several improvements in ultrasound technologies including high-resolution ultrasonography, linear transducer, radiant flow, three-/four-dimensional (3D/4D) ultrasound, speckle tracking of the fetal heart, and artificial intelligence. The aims of this review are to evaluate the use of these advanced technologies in obstetrics in the midst of new guidelines on and new techniques of obstetric ultrasonography. In particular, whether these technologies can improve the ...[more]
Similar Datasets
Project description:Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.
| S-EPMC10649694 | biostudies-literature
Project description:The study objective was to evaluate whether a novel global position system (GPS)-like position-sensing technology will enable accurate co-registration of images between imaging modalities. Co-registration of images obtained by different imaging modalities will allow for comparison and fusion between imaging modalities, and therefore has significant clinical and research implications. We compared ultrasound (US) and magnetic resonance imaging (MRI) scans of carotid endarterectomy (CEA) specimens using a novel position-sensing technology that uses an electromagnetic (EM) transmitter and sensors mounted on a US transducer. We then evaluated in vivo US-US and US-MRI co-registration.Thirteen CEA specimens underwent 3.0 Tesla MRI, after which images were uploaded to a LOGIQ E9 3D (GE Healthcare, Wauwatosa, WI) US system and registered by identifying two to three common points. A similar method was used to evaluate US-MRI co-registration in patients with carotid atherosclerosis. For carotid intima-media thickness (C-IMT) measurements, 10 volunteers underwent bilateral carotid US scans co-registered to three-dimensional US maps created on the initial visit, with a repeat scan 2 days later.For the CEA specimens, there was a mean of 20 (standard error [SE] 2.0) frames per MRI slice. The mean frame difference, over 33 registration markers, between MRI and US scans for readers 1 and 2 was -2.82 ± 19.32 and 2.09 ± 14.68 (mean ± 95% CI) frames, respectively. The US-MRI intraclass correlation coefficients (ICCs) for the first and second readers were 0.995 and 0.997, respectively. For patients with carotid atherosclerosis, the mean US frames per MRI slice (9 [SE 2.3]) was within range of that observed with CEA specimens. Inter-visit, intra-reader, and inter-reader reproducibility of C-IMT measurements were consistently high (side-averaged ICC >0.9).Accurate co-registration between US and other modalities is feasible with a GPS-like technology, which has significant clinical and research applicability.
| S-EPMC3123396 | biostudies-literature
Project description:Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.
| S-EPMC7702141 | biostudies-literature
Project description:This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance.
| S-EPMC11394308 | biostudies-literature
Project description:BackgroundResidency applications have increased in the last decade, creating growing challenges for applicants and programs.ObjectiveWe evaluated factors associated with application and match into obstetrics and gynecology residency.MethodsDuring the annual in-training examination administered to all obstetrics and gynecology residents in the United States, residents were surveyed on the residency application process.ResultsNinety-five percent (5094 of 5347) residents responded to the survey. Thirty-six percent reported applying to 30 or fewer programs, 26.7% applied to more than 31 programs, and 37.1% opted not to answer this question. Forty-nine percent of residents received honors in their obstetrics and gynecology clerkship and 37.1% did not. The majority of residents (88.6%) reported scoring between 200 and 250 on USMLE Step 1. Eighty-six percent matched into one of their top 5 programs. The only factor associated with matching in residents' top 5 programs was receiving honors in their clerkship (OR 1.29; 95% CI 1.08-1.54; P < .005). The only factor associated with matching below the top 5 programs was a couples match (OR 0.56; 95% CI 0.43-0.72; P < .001). In choosing where to apply, residents identified program location and reputation as the most important factors, while for ranking, location and residency culture were the most important.ConclusionsMost obstetrics and gynecology residents reported matching into their top 5 choices. Receiving an honors grade in the clerkship was the only factor associated with matching in applicants' top 5 programs. Location was the most important factor for applying to and ranking of programs.
| S-EPMC8054595 | biostudies-literature
Project description:Human induced pluripotent stem cell (iPSC)-derived lung organoids, engineered to carry targeted genes, offer a robust platform for investigating mechanistic insights in lung research. Although lentiviral vectors (LVVs) are highly effective for stable expression due to their integrative properties, achieving efficient transduction in human iPSC-derived lung organoids poses a significant technical challenge, likely due to the complex structure of these organoids. In this study, we optimized a method to enhance LVV transduction efficiency by physically disrupting the organoids to increase surface area, followed by spinoculation to apply shear force during cell dissociation. This approach, combined with the use of an optimized culture medium, significantly improved transduction efficiency. The success of this method was validated at both the gene and protein levels using single-cell RNA sequencing (scRNA-seq) and various cellular and molecular assays. Our optimized transduction protocol may provide a valuable tool for investigating specific cellular and molecular mechanisms in development and disease models using human iPSCs-derived lung organoids.
2025-01-02 | GSE284689 | GEO
Project description:Rapid advances in nanopore technologies for sequencing single long DNA and RNA molecules have led to substantial improvements in accuracy, read length and throughput. These breakthroughs have required extensive development of experimental and bioinformatics methods to fully exploit nanopore long reads for investigations of genomes, transcriptomes, epigenomes and epitranscriptomes. Nanopore sequencing is being applied in genome assembly, full-length transcript detection and base modification detection and in more specialized areas, such as rapid clinical diagnoses and outbreak surveillance. Many opportunities remain for improving data quality and analytical approaches through the development of new nanopores, base-calling methods and experimental protocols tailored to particular applications.
| S-EPMC8988251 | biostudies-literature
Project description:As new biomedical technologies are developed, the amount of publically available biomedical data continues to increase. To help manage these vast and disparate data sources, researchers have turned to the Semantic Web. Specifically, ontologies are used in data annotation, natural language processing, information retrieval, clinical decision support, and data integration tasks. The development of software applications to perform these tasks requires the integration of Web services to incorporate the wide variety of ontologies used in the health care and life sciences. The National Center for Biomedical Ontology, a National Center for Biomedical Computing created under the NIH Roadmap, developed BioPortal, which provides access to one of the largest repositories of biomedical ontologies. The NCBO Web services provide programmtic access to these ontologies and can be grouped into four categories; Ontology, Mapping, Annotation, and Data Access. The Ontology Web services provide access to ontologies, their metadata, ontology versions, downloads, navigation of the class hierarchy (parents, children, siblings) and details of each term. The Mapping Web services provide access to the millions of ontology mappings published in BioPortal. The NCBO Annotator Web service "tags" text automatically with terms from ontologies in BioPortal, and the NCBO Resource Index Web services provides access to an ontology-based index of public, online data resources. The NCBO Widgets package the Ontology Web services for use directly in Web sites. The functionality of the NCBO Web services and widgets are incorporated into semantically aware applications for ontology development and visualization, data annotation, and data integration. This overview will describe these classes of applications, discuss a few examples of each type, and which NCBO Web services are used by these applications.
| S-EPMC3633000 | biostudies-literature
Project description:Synthetic DNA is a growing alternative to electronic-based technologies in fields such as data storage, product tagging, or signal processing. Its value lies in its characteristic attributes, namely Watson-Crick base pairing, array synthesis, sequencing, toehold displacement and polymerase chain reaction (PCR) capabilities. In this review, we provide an overview of the most prevalent applications of synthetic DNA that could shape the future of information technology. We emphasize the reasons why the biomolecule can be a valuable alternative for conventional electronic-based media, and give insights on where the DNA-analog technology stands with respect to its electronic counterparts.
| S-EPMC8763860 | biostudies-literature
Project description:ImportanceState-specific abortion restrictions currently affect the training of approximately 44% of obstetrics and gynecology (OBGYN) residents in the US. Examination of where future trainees apply for residency is important.ObjectiveTo assess changes in the percentage of applicants to OBGYN residency programs by state based on abortion restrictions in place after the Dobbs v Jackson Women's Health Organization (hereafter, Dobbs v Jackson) US Supreme Court decision and examine whether applicants' preference for programs, as suggested by the distribution of application signals that express higher interest, was associated with abortion bans.Design, setting, and participantsThis serial cross-sectional study used anonymized data for all applicants to OBGYN residency programs in the US during September and October from 2019 to 2023. Data were obtained from the Association of American Medical Colleges Electronic Residency Application Service.ExposuresApplications and program preference signals sent to OBGYN residency programs, analyzed by applicants' self-reported demographics.Main outcomes and measuresThe primary outcome was differences in the percentage of unique applicants to OBGYN residency programs from 2019 to 2023, with programs categorized by state-based abortion restrictions after the Dobbs v Jackson decision. Secondary outcomes included the distribution of program signals by state abortion ban status.ResultsA total of 2463 applicants (2104 [85.4%] women) who applied to OBGYN programs for the 2023 residency match cycle were the focal sample of this study. While overall applicant numbers remained stable between 2019 and 2023, the number of applicants differed significantly by state abortion ban status in the 2022 (F2,1087 = 10.82; P < .001) and the 2023 (F2,1087 = 14.31; P < .001) match cycles. There were no differences in the number of signals received by programs in states with bans after controlling for known covariates such as number of applications received and program size, and there were no differences in the percentage of signals sent by out-of-state applicants to programs in states with different abortion laws than their home states (F2,268 = 2.41; P = .09).Conclusions and relevanceIn this cross-sectional study, there was a small but statistically significant decrease in the number of applicants to OBGYN residency programs in states with abortion bans in 2023 compared with 2022. However, applicant signaling data did not vary by states' abortion ban status. While OBGYN residency programs almost completely filled in 2023, continued monitoring for the potential consequences of state abortion bans for OBGYN training is needed.
| S-EPMC10851098 | biostudies-literature