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  1. Article: Supporting Communities in Humanitarian Crises with Acupuncture and Integrative Medicine A Perspective.

    Budd, Sarah

    Medical acupuncture

    2023  Volume 35, Issue 4, Page(s) 159–162

    Abstract: My background is in nursing, midwifery, and acupuncture. In November of 2021, I came across a blog post about volunteering as an acupuncturist in a rehabilitation clinic for migrant refugees and asylum seekers on the island of Lesvos. With experience in ... ...

    Abstract My background is in nursing, midwifery, and acupuncture. In November of 2021, I came across a blog post about volunteering as an acupuncturist in a rehabilitation clinic for migrant refugees and asylum seekers on the island of Lesvos. With experience in the National Acupuncture Detoxification Association (NADA) protocol and trauma training provided by Acupuncturists Without Borders, I decided to apply to the nongovernmental organization Earth Medicine rehabilitation clinic. I stayed for 2 weeks in January 2022, and went again in September 2022 for 2 weeks, but that time, I was based inside the camp. On returning home from my first trip, and while giving a talk about Lesvos to our regional group, a fellow acupuncturist suggested that we could do something closer to home. Thus, we set up a project in our city, Exeter, in the United Kingdom. Thanks to a willing team of volunteers, asylum seekers and refugees are offered free acupuncture treatments weekly on Saturday mornings. This takes place in a community center in a group setting, using the NADA ear protocol, as well as other acupuncture points and occasional full-body treatments when the circumstances allow this. Although the work on Lesvos was hard, it was also very rewarding. Working there has had a profound effect on me and I plan to go back. At our local project, we receive very positive feedback from the people who come to us. Using acupuncture to address post-traumatic stress disorder is very worthwhile, and I encourage others to consider doing the same.
    Language English
    Publishing date 2023-08-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2296110-0
    ISSN 1933-6594 ; 1933-6586
    ISSN (online) 1933-6594
    ISSN 1933-6586
    DOI 10.1089/acu.2023.0012
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  2. Article ; Online: Sonographer interaction with artificial intelligence: collaboration or conflict?

    Day, T G / Matthew, J / Budd, S / Hajnal, J V / Simpson, J M / Razavi, R / Kainz, B

    Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology

    2023  Volume 62, Issue 2, Page(s) 167–174

    MeSH term(s) Humans ; Artificial Intelligence ; Ultrasonography
    Language English
    Publishing date 2023-07-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 1073183-0
    ISSN 1469-0705 ; 0960-7692
    ISSN (online) 1469-0705
    ISSN 0960-7692
    DOI 10.1002/uog.26238
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3214: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: A survey on active learning and human-in-the-loop deep learning for medical image analysis.

    Budd, Samuel / Robinson, Emma C / Kainz, Bernhard

    Medical image analysis

    2021  Volume 71, Page(s) 102062

    Abstract: Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment ... ...

    Abstract Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.
    MeSH term(s) Deep Learning ; Humans ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2021-04-09
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2021.102062
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5206: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Interactive effects of protonated nicotine concentration and device power on ENDS nicotine delivery, puff topography, and subjective effects.

    Eversole, Alisha / Budd, Serenity / Karaoghlanian, Nareg / Lipato, Thokozeni / Eissenberg, Thomas / Breland, Alison B

    Experimental and clinical psychopharmacology

    2022  Volume 31, Issue 2, Page(s) 443–454

    Abstract: Electronic nicotine delivery systems (ENDSs) produce an aerosol by heating a liquid that often contains nicotine. The nicotine can be protonated that may make the aerosol easier to inhale than freebase nicotine. This study's purpose is to determine, in ... ...

    Abstract Electronic nicotine delivery systems (ENDSs) produce an aerosol by heating a liquid that often contains nicotine. The nicotine can be protonated that may make the aerosol easier to inhale than freebase nicotine. This study's purpose is to determine, in cigarette smokers and ENDS users, the effects of three concentrations of protonated nicotine aerosolized at two different power settings. Forty-five participants (22 cigarette smokers and 23 ENDS users) completed some or all of six sessions that varied by liquid nicotine concentration (10, 15, or 30 mg/ml protonated nicotine) and device power (15 or 30 W). Participants took 10 puffs from each product and then used each product for 90 min
    MeSH term(s) Humans ; Nicotine ; Smoking ; Smokers ; Electronic Nicotine Delivery Systems ; Tobacco Products
    Chemical Substances Nicotine (6M3C89ZY6R)
    Language English
    Publishing date 2022-06-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1209960-0
    ISSN 1936-2293 ; 1064-1297
    ISSN (online) 1936-2293
    ISSN 1064-1297
    DOI 10.1037/pha0000576
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4462: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: The baseline risk of multiple febrile seizures in the same febrile illness: a meta-analysis.

    Henry, Christopher / Cockburn, Chelsea / Simpson, Mary Helen / Budd, Serenity / Wang, Chen / Dinov, Darina

    European journal of pediatrics

    2022  Volume 181, Issue 6, Page(s) 2201–2213

    Abstract: The baseline risk for multiple febrile seizures within the same febrile illness is largely unknown. Estimates range from 5 to 30%. Imprecise estimates can lead to incorrectly powering studies investigating the management of febrile seizures. To estimate ... ...

    Abstract The baseline risk for multiple febrile seizures within the same febrile illness is largely unknown. Estimates range from 5 to 30%. Imprecise estimates can lead to incorrectly powering studies investigating the management of febrile seizures. To estimate the risk of multiple febrile seizures in the same febrile illness, we systematically reviewed and conducted a meta-analysis of studies from January 2000 to December 2021 that contained data for the number of children for both simple and complex febrile seizures in the same febrile illness. We searched MEDLINE, EMBASE, and Web of Science for randomized, quasi-randomized, prospective, and retrospective trials that involved children with febrile seizures. A total of 23,131 febrile illnesses with febrile seizures met the inclusion criteria. The estimated baseline risk of multiple febrile seizures in the same febrile illness was 17% (95% CI, 16-19%). However, the 30 cohorts that included both admitted and non-admitted patients had a lower percentage of multiple FSs within the same illness (14%; 95% CI, 12-15%) than the 30 cohorts that enrolled only admitted patients (20%; 95% CI, 16-25%).
    Conclusion: Researchers can use estimates in this paper to design future studies. Taking into the account the substantial heterogeneity between countries and studies, clinicians could cautiously use our estimates in their clinical assessment and be better able to set parental expectations about a child's chances of having another febrile seizure during the current illness.
    Trial registration: PROSPERO CRD42020191784. Registered July 18, 2020.
    What is known: • There is renewed interest in the diagnostic workup and prophylactic treatment of febrile seizures to prevent repeat seizures in the same febrile illness. • There is a lack of accurate estimates of the baseline risk for multiple febrile seizures in the same illness to properly design studies investigating management.
    What is new: • This study provides the most robust estimates for the baseline risk for multiple febrile seizures in the same illness.
    MeSH term(s) Child ; Hospitalization ; Humans ; Prospective Studies ; Retrospective Studies ; Seizures, Febrile/diagnosis ; Seizures, Febrile/epidemiology ; Seizures, Febrile/etiology
    Language English
    Publishing date 2022-03-16
    Publishing country Germany
    Document type Journal Article ; Meta-Analysis
    ZDB-ID 194196-3
    ISSN 1432-1076 ; 0340-6199 ; 0943-9676
    ISSN (online) 1432-1076
    ISSN 0340-6199 ; 0943-9676
    DOI 10.1007/s00431-022-04431-w
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    In stock of ZB MED Cologne/Königswinter

    Um IV Zs.75: Show issues Location:
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  6. Article: The shark behind the sofa: the psychoanalytic theory of dreams.

    Budd, S

    History workshop journal : HWJ

    2001  , Issue 48, Page(s) 133–150

    MeSH term(s) Dreams ; History, 20th Century ; Psychoanalysis/history ; Symbolism
    Language English
    Publishing date 2001-09-11
    Publishing country England
    Document type Biography ; Historical Article ; Journal Article
    ZDB-ID 2076082-6
    ISSN 1477-4569 ; 1363-3554
    ISSN (online) 1477-4569
    ISSN 1363-3554
    DOI 10.1093/hwj/1999.48.133
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  7. Article ; Online: Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

    Day, T G / Matthew, J / Budd, S F / Venturini, L / Wright, R / Farruggia, A / Vigneswaran, T V / Zidere, V / Hajnal, J V / Razavi, R / Simpson, J M / Kainz, B

    Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology

    2024  

    Abstract: Objectives: Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. ... ...

    Abstract Objectives: Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. Giving additional information about AI model workings, such as confidence scores for AI predictions, may be a way of improving performance further. Our aims were to investigate whether AI advice improved overall diagnostic accuracy (using a single CHD lesion as an exemplar), and to see what, if any, additional information given to clinicians optimized the overall performance of the clinician-AI team.
    Methods: An AI model was trained to classify a single fetal CHD lesion (atrioventricular septal defect, AVSD), using a retrospective cohort of 121,130 cardiac four chamber images extracted from 173 ultrasound scan videos (98 with normal hearts, 75 with AVSD). A ResNet50 model architecture was used. Temperature scaling of model prediction probability was performed on a validation set, and gradient-weighted class activation maps (grad-CAMs) produced. Ten clinicians (two consultant fetal cardiologists, three trainees in pediatric cardiology, and five fetal cardiac sonographers) were recruited from a center of fetal cardiology to participate. Each participant was shown 2000 fetal four chamber images in a random order (1,000 normal and 1,000 AVSD). The dataset was comprised of 500 images, each shown in four conditions: 1) image alone without AI output; 2) image with binary AI classification; 3) image with AI model confidence; 4) image with gradient-weighted class activation map image overlays. The clinicians were asked to classify each image as normal or AVSD.
    Results: 20,000 image classifications were recorded from 10 clinicians. The AI model alone achieved an accuracy of 0.798 (95% CI 0.760 - 0.832), sensitivity of 0.868 (95% CI 0.834 - 0.902) and specificity of 0.728 (95% CI 0.702 - 0.754, and the clinicians without AI achieved an accuracy of 0.844 (95% CI 0.834 - 0.854), sensitivity of 0.827 (95% CI 0.795 - 0.858) and specificity of 0.861 (95% CI 0.828 - 0.895). Showing a binary (normal or AVSD) AI model output resulted in significant improvement in accuracy to 0.865 (p <0.001). This effect was seen in both experienced and less experienced participants. Giving incorrect AI advice resulted in significant deterioration in overall accuracy from 0.761 to 0.693 (p <0.001), which was driven by an increase in both type I and type II error by the clinicians. This effect was worsened by showing model confidence (accuracy 0.649, p <0.001) or grad-CAM (accuracy 0.644, p <0.001).
    Conclusions: AI has the potential to improve performance when used in collaboration with clinicians, even if the model performance does not reach expert level. Giving additional information about model workings such as model confidence and class activation map image overlays did not improve overall performance, and actually worsened performance for images where the AI model was incorrect. This article is protected by copyright. All rights reserved.
    Language English
    Publishing date 2024-01-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 1073183-0
    ISSN 1469-0705 ; 0960-7692
    ISSN (online) 1469-0705
    ISSN 0960-7692
    DOI 10.1002/uog.27577
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3214: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Book ; Online: Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans

    Venturini, Lorenzo / Budd, Samuel / Farruggia, Alfonso / Wright, Robert / Matthew, Jacqueline / Day, Thomas G. / Kainz, Bernhard / Razavi, Reza / Hajnal, Jo V.

    2024  

    Abstract: The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by ... ...

    Abstract The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a convolutional neural network to classify each frame of an ultrasound video recording. We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. We performed a retrospective experiment on 1457 recordings (comprising 48 million frames) of 20-week ultrasound scans, estimated fetal biometrics in those scans and compared our estimates to the measurements sonographers took during the scan. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals in which the true biometric value is expected to lie.

    Comment: 14 pages, 16 figures. Submitted to NPJ digital medicine. For associated video file, see http://wp.doc.ic.ac.uk/ifind/wp-content/uploads/sites/79/2023/12/realtime.gif
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; I.4.7 ; J.3
    Publishing date 2024-01-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: Moxibustion for breech presentation.

    Budd, S

    Complementary therapies in nursing & midwifery

    2000  Volume 6, Issue 4, Page(s) 176–179

    Abstract: Breech presentation at term is considered a possible obstetric complication, and the management before and during labour remains controversial. A technique called 'moxibustion' is used in traditional Chinese medicine to encourage version of the fetus in ... ...

    Abstract Breech presentation at term is considered a possible obstetric complication, and the management before and during labour remains controversial. A technique called 'moxibustion' is used in traditional Chinese medicine to encourage version of the fetus in breech presentation. It has been used in the maternity unit in Plymouth for 11 years. The results would seem to suggest it may have a positive effect and play a part in reducing the number of breech presentations at term and therefore also a reduction in the number of caesarean sections which are so often advocated in breech presentation. This article describes the technique in greater detail and discusses the potential for the future.
    MeSH term(s) Artemisia ; Breech Presentation ; Contraindications ; Female ; Fetal Movement/drug effects ; Humans ; Midwifery/methods ; Moxibustion/methods ; Phytotherapy/methods ; Pregnancy ; Pregnancy Trimester, Third
    Language English
    Publishing date 2000-11
    Publishing country Scotland
    Document type Case Reports ; Journal Article ; Review
    ZDB-ID 1433698-4
    ISSN 1353-6117
    ISSN 1353-6117
    DOI 10.1054/ctnm.2000.0505
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4912: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?

    Day, Thomas G / Budd, Samuel / Tan, Jeremy / Matthew, Jacqueline / Skelton, Emily / Jowett, Victoria / Lloyd, David / Gomez, Alberto / Hajnal, Jo V / Razavi, Reza / Kainz, Bernhard / Simpson, John M

    Prenatal diagnosis

    2023  

    Abstract: Background: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compare ... ...

    Abstract Background: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compare with our own AI model.
    Methods: Current screening programme performance was calculated from local and national sources. AI models were trained using four-chamber ultrasound views of the fetal heart, using a ResNet classifier.
    Results: Estimated current fetal screening programme sensitivity and specificity for HLHS were 94.3% and 99.985%, respectively. Depending on calibration, AI models to detect HLHS were either highly sensitive (sensitivity 100%, specificity 94.0%) or highly specific (sensitivity 93.3%, specificity 100%). Our analysis suggests that our highly sensitive model would generate 45,134 screen positive results for a gain of 14 additional HLHS cases. Our highly specific model would be associated with two fewer detected HLHS cases, and 118 fewer false positives.
    Conclusion: If used independently, our AI model performance is slightly worse than the performance level of the current screening programme in detecting HLHS, and this performance is likely to deteriorate further when used prospectively. This demonstrates that collaboration between humans and AI will be key for effective future clinical use.
    Language English
    Publishing date 2023-09-30
    Publishing country England
    Document type Journal Article
    ZDB-ID 82031-3
    ISSN 1097-0223 ; 0197-3851
    ISSN (online) 1097-0223
    ISSN 0197-3851
    DOI 10.1002/pd.6445
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