A Phase IV, open-label, non-randomized, multicenter study to evaluate safety and efficacy of intravenous administration of Sonazoid™ for contrast-enhanced ultrasound liver imaging in paediatric patients

开始日期2024-07-29

申办/合作机构

GE Healthcare AG

CTRI/2023/05/053235 / RecruitingN/A

A single-arm, multicentre, Post-marketing observational follow up on patients who have been given Gadolinium for clinical scans to detect all prospective adverse events immediately post dose and 6 (Â±2) weeks after administration of Omniscan

开始日期2023-06-05

申办/合作机构

GE Healthcare AG

EUCTR2008-005219-16-GB / Not yet recruiting临床2期

A Phase 2, Open-label, Proof-of-concept Study to Assess the Ability to Detect Tumours and Angiogenesis via the Expression of avb3 Integrin Receptors by [18F]AH111585 PET Imaging

开始日期2010-12-06

申办/合作机构

GE Healthcare AG

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0 项与 GE Healthcare AG 相关的专利（医药）

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项与 GE Healthcare AG 相关的文献（医药）

2024-10-01·Emergency Medicine Journal

Evaluation of the impact of artificial intelligence-assisted image interpretation on the diagnostic performance of clinicians in identifying pneumothoraces on plain chest X-ray: a multi-case multi-reader study

Article

作者: Cowell, Gordon W ; Bahra, Jasdeep ; Novak, Alex ; Baenen, Alec ; Gleeson, Fergus ; Maskell, Giles ; Ather, Sarim ; Taberham, Rhona ; Oke, Jason ; Aylward, Peter ; Bloomfield, Claire ; Hallifax, Rob ; Akrama, Osama ; Bailey, Jon ; Bhatti, Saher ; Jarral, Waqas ; Gill, Avneet ; Gamble, Olivia ; Wrightson, John ; Banerji, Abhishek ; Astley, Kerry ; Shadmaan, Amied ; Barry, Steven ; Ansaripour, Ali ; Duggan, Tom ; Spence, Nathan ; James, Antonia ; Johnson, Hilal ; Beggs, Mark ; Keevill, Melissa ; Belcher, Elizabeth ; Ghelman, Sharon ; Espinosa Morgado, Abdala Trinidad

BackgroundArtificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms’ impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX).MethodsA multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a ‘washout’ period, this process was repeated including the AI output.ResultsAnalysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01).ConclusionThe study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.

2022-06-01·Journal of Clinical Oncology

Rolling window-based hepatitis toxicity prediction from routine bloodwork in patients undergoing immune checkpoint inhibitor therapy.

作者: Park, Ben Ho ; Horváth, Gergely ; Csernai, Eszter ; Osterman, Travis John ; Smith, David ; Mittendorf, Kathleen ; Wolber, Jan ; LeNoue-Newton, Michele

e13565 Background: Hepatitis toxicity is one of the most important adverse effects of immune checkpoint inhibitor (ICI) therapy, occurring in approximately 10% of patients. However, when identified early, it can be managed clinically, potentially allowing continuation of ICI treatment. The goal of the study was to evaluate the feasibility and clinical usefulness of an artificial intelligence (AI) model to predict the risk of developing hepatitis toxicity during the course of ICI treatment from routine bloodwork values. Methods: Our model uses a clinical dataset of 2438 patients who received ICI treatment at the Vanderbilt University Medical Center prior to the end of 2020. Hepatitis toxicity was defined as one or more of ALT, AST, ALKPHOS, BILIRUBIN values exceeding 2.5-times the upper limit of normal value. The available feature set was limited to the routinely available blood test values. All features were normalized to the upper limit of normal and transformed to a discretized symbolic representation, a modified version of Symbolic Aggregate ApproXimation. Motifs were extracted as n-grams from the symbol series, and the counts were used as input features for the predictive model. The study uses standard data science model training and evaluation concepts: train, validation, and test splits were created randomly on the patient level; the reported evaluation metrics are median AUC, TPR, TNR, PPV, NPV over 10 sampling runs. The final, best-performing model architecture is a boosted decision tree model (XGBoost) trained on the last four blood tests to predict hepatitis at the next blood sampling timepoint (i.e., at the time of the next ICI treatment appointment). Results: The best model uses the following eight blood values as features: ALT, AST, ALKPHOS, BILIRUBIN, ALBUMIN, CO2, CALCIUM, and BUN, and achieves an AUC of 0.82 (std. 0.01), with TPR = 0.32 (0.03), TNR = 0.97 (0.005), PPV = 0.18 (0.03), and NPV = 0.99 (0.002). It finds 32% of the timepoints where the patient is going to develop hepatitis toxicity prior to their next treatment, and about 1 in 5 positive predictions are correct. It is important to note that only about 1% of all ‘sequences’ of four consecutive blood tests are followed by hepatitis at the next test. That is, while a relatively large proportion of patients are going to develop hepatitis toxicity during their ICI treatment, the timepoint at which this happens is very uncertain. Conclusions: We demonstrate that an AI model built using only already available patient laboratory data could provide clinically useful input for clinicians to support their ICI treatment decisions to reduce the occurrence of hepatitis toxicity. The dynamic nature and below-patient-level granularity of the model would allow a clinician / clinical trial investigator to make adjustments to the therapy based on individual patient reaction over time.

2018-12-01·EJNMMI Research

Simplifying [18F]GE-179 PET: are both arterial blood sampling and 90-min acquisitions essential?

Article

作者: Riaño Barros, Daniela A ; Duncan, John S ; Hinz, Rainer ; Trigg, William ; Hammers, Alexander ; Koepp, Matthias J ; McGinnity, Colm J ; Brooks, David J

INTRODUCTIONThe NMDA receptor radiotracer [¹⁸F]GE-179 has been used with 90-min scans and arterial plasma input functions. We explored whether (1) arterial blood sampling is avoidable and (2) shorter scans are feasible.METHODSFor 20 existing [¹⁸F]GE-179 datasets, we generated (1) standardised uptake values (SUVs) over eight intervals; (2) volume of distribution (V_T) images using population-based input functions (PBIFs), scaled using one parent plasma sample; and (3) V_T images using three shortened datasets, using the original parent plasma input functions (ppIFs).RESULTSCorrelations with the original ppIF-derived 90-min V_Ts increased for later interval SUVs (maximal ρ = 0.78; 80-90 min). They were strong for PBIF-derived V_Ts (ρ = 0.90), but between-subject coefficient of variation increased. Correlations were very strong for the 60/70/80-min original ppIF-derived V_Ts (ρ = 0.97-1.00), which suffered regionally variant negative bias.CONCLUSIONSWhere arterial blood sampling is available, reduction of scan duration to 60 min is feasible, but with negative bias. The performance of SUVs was more consistent across participants than PBIF-derived V_Ts.

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