Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.

Abstract: The transfer of neutrons onto Ne-24 has been measured using a reaccelerated radioactive beam of Ne-24 to study the (d, p) reaction in inverse kinematics. The unusual raising of the first 3/2(+) level in Ne-25 and its significance in terms of the migration of the neutron magic number from N = 20 to N = 16 is put on a firm footing by confirmation of this state's identity. The raised 3/2(+) level is observed simultaneously with the intruder negative parity 7/2(-) and 3/2(-) levels, providing evidence for the reduction in the N = 20 gap. The coincident gamma-ray decays allowed the assignment of spins as well as the transferred orbital angular momentum. The excitation energy of the 3/2(+) state shows that the established USD shell model breaks down well within the sd model space and requires a revised treatment of the proton-neutron monopole interaction.

Abstract: The gamma rays and conversion electrons emitted in the beta decay of (78)Sr to levels in (78)Rb have been studied using Ge detectors and a mini-orange spectrometer. A reliable level scheme based on the results of these experiments has been established. The properties of the levels in (78)Rb have been compared with calculations based on deformed Hartree-Fock with Skyrme interactions and pairing correlations in the BCS approximation. This has allowed an interpretation of the nature of the observed sets of levels in the odd-odd nucleus (78)Rb.

Abstract: A study of the Gamow-Teller strength distributions B(GT) in the beta decay of Sr-78 and Rb-76,Rb-78 has been made using a total absorption spectrometer (TAS). Following the success in deducing the sign of the deformation for Sr-76, a similar approach is adopted for Sr-78 based on a comparison of the measured B(GT) with quasiparticle random-phase approximation calculations. This work confirms its previously expected prolate deformation in the ground state. Conclusions about the structure of the odd-odd Rb-76,Rb-78 isotopes have been drawn based on their measured B(GT) distributions.

Abstract: The neutron-induced fission cross section of Th-230 has been measured at the neutron time-of-flight facility n_TOF located at CERN. The experiment was performed at the experimental area EAR-1 with a neutron flight path of 185 m, using Micromegas detectors for the detection of the fission fragments. The Th-230(n, f ) cross section was determined relative to the U-235(n, f ) one, covering the energy range from the fission threshold up to 400 MeV. The results from the present work are compared with existing cross-section datasets and the observed discrepancies are discussed and analyzed. Finally, using the code EMPIRE 3.2.3 a theoretical study, based on the statistical model, was performed leading to a satisfactory reproduction of the experimental results with the proper tuning of the respective parameters, while for incident neutron energy beyond 200 MeV the fission of( 230)Th was described by Monte Carlo simulations.