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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Keywords |
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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. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
ISBN |
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Conference |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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Author |
Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C. |
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Title |
Machine Learning aided 3D-position reconstruction in large LaCl3 crystals |
Type |
Journal Article |
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Year |
2021 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
1001 |
Issue |
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Pages |
165249 - 17pp |
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Keywords |
Gamma-ray; Position sensitive detectors; Monolithic crystals; Compton imaging; Machine Learning; Convolutional Neural Networks; Total Energy Detector; Neutron capture cross-section |
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Abstract |
We investigate five different models to reconstruct the 3D gamma-ray hit coordinates in five large LaCl3(Ce) monolithic crystals optically coupled to pixelated silicon photomultipliers. These scintillators have a base surface of 50 x 50 mm(2) and five different thicknesses, from 10 mm to 30 mm. Four of these models are analytical prescriptions and one is based on a Convolutional Neural Network. Average resolutions close to 1-2 mm fwhm are obtained in the transverse crystal plane for crystal thicknesses between 10 mm and 20 mm using analytical models. For thicker crystals average resolutions of about 3-5 mm fwhm are obtained. Depth of interaction resolutions between 1 mm and 4 mm are achieved depending on the distance of the interaction point to the photosensor surface. We propose a Machine Learning algorithm to correct for linearity distortions and pin-cushion effects. The latter allows one to keep a large field of view of about 70%-80% of the crystal surface, regardless of crystal thickness. This work is aimed at optimizing the performance of the so-called Total Energy Detector with Compton imaging capability (i-TED) for time-of-flight neutron capture cross-section measurements. |
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Address |
[Balibrea-Correa, J.; Lerendegui-Marco, J.; Babiano-Suarez, V.; Caballero, L.; Calvo, D.; Ladarescu, I.; Olleros-Rodriguez, P.; Domingo-Pardo, C.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: javier.balibrea@ific.uv.es |
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Elsevier |
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English |
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ISSN |
0168-9002 |
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Conference |
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Notes |
WOS:000641308300007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
4803 |
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Author |
Babiano-Suarez, V. et al; Lerendegui-Marco, J.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Real, D.; Domingo-Pardo, C. |
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Title |
Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques |
Type |
Journal Article |
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Year |
2021 |
Publication |
European Physical Journal A |
Abbreviated Journal |
Eur. Phys. J. A |
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Volume |
57 |
Issue |
6 |
Pages |
197 - 17pp |
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Keywords |
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Abstract |
i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, gamma) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the Au-197(n, gamma) and Fe-56(n, gamma) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of similar to 3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and newanalysis methodologies based on Machine-Learning techniques. |
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Series Volume |
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Edition |
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ISSN |
1434-6001 |
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Notes |
WOS:000662881100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4875 |
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Author |
n_TOF Collaboration (Domingo-Pardo, C. et al); Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Ladarescu, I.; Lerendegui-Marco, J.; Tain, J.L.; Tarifeño-Saldivia, A. |
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Title |
Advances and new ideas for neutron-capture astrophysics experiments at CERN n_TOF |
Type |
Journal Article |
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Year |
2023 |
Publication |
European Physical Journal A |
Abbreviated Journal |
Eur. Phys. J. A |
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Volume |
59 |
Issue |
1 |
Pages |
8 - 11pp |
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Keywords |
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Abstract |
This article presents a few selected developments and future ideas related to the measurement of (n, gamma ) data of astrophysical interest at CERN n_TOF. The MC-aided analysis methodology for the use of low-efficiency radiation detectors in time-of-flight neutron-capture measurements is discussed, with particular emphasis on the systematic accuracy. Several recent instrumental advances are also presented, such as the development of total-energy detectors with gamma- ray imaging capability for background suppression, and the development of an array of small-volume organic scintilla tors aimed at exploiting the high instantaneous neutron-flux of EAR2. Finally, astrophysics prospects related to the intermediate i neutron-capture process of nucleosynthesis are discussed in the context of the new NEAR activation area. |
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Address |
[Domingo-Pardo, C.; Babiano-Suarez, V.; Balibrea-Correa, J.; Caballero, L.; Ladarescu, I.; Lerendegui-Marco, J.; Tain, J. L.; Tarifeno-Saldivia, A.] Univ Valencia, CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: domingo@ific.uv.es |
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Publisher |
Springer |
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English |
Summary Language |
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ISSN |
1434-6001 |
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Notes |
WOS:000926364900001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5479 |
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Author |
n_TOF Collaboration (Sosnin, N.V. et al.); Babiano-Suarez, V.; Caballero, L.; Domingo-Pardo, C.; Ladarescu, I.; Tain, J.L. |
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Title |
Measurement of the 77Se(n,gamma) cross section up to 200 keV at the n_TOF facility at CERN |
Type |
Journal Article |
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Year |
2023 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
107 |
Issue |
6 |
Pages |
065805 - 9pp |
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Keywords |
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Abstract |
The 77Se(n,gamma) reaction is of importance for 77Se abundance during the slow neutron capture process in massive stars. We have performed a new measurement of the 77Se radiative neutron capture cross section at the Neutron Time-of-Flight facility at CERN. Resonance capture kernels were derived up to 51 keV and cross sections up to 200 keV. Maxwellian-averaged cross sections were calculated for stellar temperatures between kT = 5 keV and kT = 100 keV, with uncertainties between 4.2% and 5.7%. Our results lead to substantial decreases of 14% and 19% in 77Se abundances produced through the slow neutron capture process in selected stellar models of 15M0 and 2M0, respectively, compared to using previous recommendation of the cross section. |
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Address |
[V. Sosnin, N.; Lederer-Woods, C.; Garg, R.; Dietz, M.; Murphy, A. St. J.; Lonsdale, S.; Woods, P. J.] Univ Edinburgh, Sch Phys & Astron, Edinburgh, Scotland, Email: nsosnin@ed.ac.uk |
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Publisher |
Amer Physical Soc |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2469-9985 |
ISBN |
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Conference |
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Notes |
WOS:001023903800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5599 |
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| Permanent link to this record |
