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The MLLTRAP is a Penning trap system designed to perform highaccuracy nuclear mass measurements in its final stage aiming to use highlycharged ions. The goal is to reach an accuracy of 10-10, which is required for high-precision fundamental physics studies like the determination of fundamental constants and measurements of electron binding energies for QED at strong fields. In the first development stage the setup will work with singly-charged ions. First results of systematic uncertainty investigations are presented. [ABSTRACT FROM AUTHOR]

The Penning-trap mass spectrometer SHIPTRAP allows precision mass measurements of rare isotopes produced in fusion-evaporation reactions. In the first period of operation the masses of more than 50 neutron-deficient radionuclides have been measured. In this paper the perspectives for direct mass measurements of rare isotopes around nobelium are discussed and the achievable precision is addressed. The temporal stability of the magnetic field, an important issue for the long measurement times resulting from the low production rates, was investigated and the time-dependent uncertainty due to magnetic field fluctuations was determined. Based on the present performance direct mass measurements of nobelium isotopes are already feasible. With several technical improvements heavier elements between Z=102–105 will be in reach. [ABSTRACT FROM AUTHOR]

Direct mass measurements of nuclides near to the supposed end-point region of the astrophysical rp-process were performed at SHIPTRAP, the Penning trap mass spectrometer at GSI Darmstadt. The masses of 24 nuclides were measured with relative uncertainties between 5 . 10-8 and 2 . 10-7 . Three of them, 107Sb , 111I and 112I , were determined experimentally for the first time. The data analysis and mass evaluation are presented in detail. [ABSTRACT FROM AUTHOR]

Objective: This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data., Methods: For the independent dataset, 718 dental images representing different stages of carious ( n = 535) and noncarious teeth ( n = 183) were retrieved from the internet. All photographs were evaluated by the dental team (reference standard) and the AI-based model (test method). Diagnostic performance was statistically determined using cross-tabulations to calculate accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC)., Results: An overall ACC of 92.0% was achieved for caries detection, with an ACC of 85.5-95.6%, SE of 42.9-93.3%, SP of 82.1-99.4% and AUC of 0.702-0.909 for the classification of caries. Furthermore, 97.0% of the cases were accurately localised. Fully and partially correct segmentation was achieved in 52.9% and 44.1% of the cases, respectively., Conclusions: The validated AI-based model showed promising diagnostic performance in detecting and classifying caries using an independent image dataset. Future studies are needed to investigate the validity, reliability and practicability of AI-based models using dental photographs from different image sources and/or patient groups.

Background/Objectives : Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual-tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs. Methods : A total of 143 anonymised photographs of anterior deciduous teeth (ECC = 107, controls = 36) were visually evaluated by the dental study group (reference test) and analysed using the AI-based model (test method). Diagnostic performance was determined statistically. Results : ECC detection accuracy was 97.2%. Diagnostic performance varied between carious lesion classes (noncavitated lesions, greyish translucency/microcavity, cavitation, destructed tooth), with accuracies ranging from 88.9% to 98.1%, sensitivities ranging from 68.8% to 98.5% and specificities ranging from 86.1% to 99.4%. The area under the curve ranged from 0.834 to 0.964. Conclusions : The performance of the AI-based model is similar to that reported for the internal dataset used by developers. Further studies with independent image samples are required to comprehensively gauge the performance of the model.

Objectives: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH)., Methods: An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method). The accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC) were determined. Furthermore, the correctness of EH/MIH lesion localisation and segmentation was evaluated., Results: An overall ACC of 94.3 % was achieved for image-based detection of EH/MIH. Cross-classification of the AI-based class prediction and the reference standard resulted in an agreement of 89.2 % for all diagnostic decisions (n = 594), with an ACC between 91.4 % and 97.8 %. The corresponding SE and SP values ranged from 81.7 % to 92.8 % and 91.9 % to 98.7 %, respectively. The AUC varied between 0.894 and 0.945. Image size had only a limited impact on diagnostic performance. The AI-based model correctly predicted EH/MIH localisation in 97.3 % of cases. For the detected lesions, segmentation was fully correct in 63.4 % of all cases and partially correct in 33.9 %., Conclusions: This study documented the promising diagnostic performance of an open-access AI tool in the detection and classification of EH/MIH in external images., Clinical Significance: Externally validated AI-based diagnostic methods could facilitate the detection of EH/MIH lesions in dental photographs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)