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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 |
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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 |
Albiol, F.; Corbi, A.; Albiol, A. |
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Title |
3D measurements in conventional X-ray imaging with RGB-D sensors |
Type |
Journal Article |
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Year |
2017 |
Publication |
Medical Engineering & Physics |
Abbreviated Journal |
Med. Eng. Phys. |
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Volume |
42 |
Issue |
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Pages |
73-79 |
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Keywords |
X-ray; Depth cameras; Epipolar geometry; 3D reconstruction; Movement tracking; Dense surface mapping |
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Abstract |
A method for deriving 3D internal information in conventional X-ray settings is presented. It is based on the combination of a pair of radiographs from a patient and it avoids the use of X-ray-opaque fiducials and external reference structures. To achieve this goal, we augment an ordinary X-ray device with a consumer RGB-D camera. The patient' s rotation around the craniocaudal axis is tracked relative to this camera thanks to the depth information provided and the application of a modern surface-mapping algorithm. The measured spatial information is then translated to the reference frame of the X-ray imaging system. By using the intrinsic parameters of the diagnostic equipment, epipolar geometry, and X-ray images of the patient at different angles, 3D internal positions can be obtained. Both the RGB-D and Xray instruments are first geometrically calibrated to find their joint spatial transformation. The proposed method is applied to three rotating phantoms. The first two consist of an anthropomorphic head and a torso, which are filled with spherical lead bearings at precise locations. The third one is made of simple foam and has metal needles of several known lengths embedded in it. The results show that it is possible to resolve anatomical positions and lengths with a millimetric level of precision. With the proposed approach, internal 3D reconstructed coordinates and distances can be provided to the physician. It also contributes to reducing the invasiveness of ordinary X-ray environments and can replace other types of clinical explorations that are mainly aimed at measuring or geometrically relating elements that are present inside the patient's body. |
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Address |
[Albiol, Francisco; Corbi, Alberto] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46003 Valencia, Spain, Email: alberto.corbi@ific.uv.es |
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Publisher |
Elsevier Sci Ltd |
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English |
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Edition |
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ISSN |
1350-4533 |
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Expedition |
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Conference |
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Notes |
WOS:000398007100008 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3043 |
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Author |
Agramunt, J. et al; Tain, J.L.; Albiol, F.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeño-Saldivia, A.; Valencia, E. |
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Title |
Characterization of a neutron-beta counting system with beta-delayed neutron emitters |
Type |
Journal Article |
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Year |
2016 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
807 |
Issue |
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Pages |
69-78 |
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Keywords |
Beta-delayed neutron emission probability; Neutron and beta counters; Self-triggered digital data acquisition system; Geant4 simulations |
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Abstract |
A new detection system for the measurement of beta-delayed neutron emission probabilities has been characterized using fission products with well known beta-delayed neutron emission properties. The setup consists of BELEN-20, a 4 pi-neutron counter with twenty He-3 proportional tubes arranged inside a large polyethylene neutron moderator, a thin Si detector for beta counting and a self-triggering digital data acquisition system. The use of delayed-neutron precursors with different neutron emission windows allowed the study of the effect of energy dependency on neutron, beta and beta-neutron rates. The observed effect is well reproduced by Monte Carlo simulations. The impact of this dependency on the accuracy of neutron emission probabilities is discussed. A new accurate value of the neutron emission probability for the important delayed-neutron precursor I-137 was obtained, P-n = 7.76(14)%. |
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Address |
[Agramunt, J.; Tain, J. L.; Albiol, E.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeno-Saldivia, A.; Valencia, E.] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46071 Valencia, Spain, Email: Tain@ific.uv.es |
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Corporate Author |
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Thesis |
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Publisher |
Elsevier Science Bv |
Place of Publication |
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English |
Summary Language |
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Original Title |
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Series Editor |
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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 |
0168-9002 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000365596200010 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2481 |
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Author |
Magan, D.L.P.; Caballero, L.; Domingo-Pardo, C.; Agramunt-Ros, J.; Albiol, F.; Casanovas, A.; Gonzalez, A.; Guerrero, C.; Lerendegui-Marco, J.; Tarifeño-Saldivia, A. |
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Title |
First tests of the applicability of gamma-ray imaging for background discrimination in time-of-flight neutron capture measurements |
Type |
Journal Article |
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Year |
2016 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
823 |
Issue |
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Pages |
107-119 |
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Keywords |
Neutron capture cross-sections; gamma-ray imaging; Total energy detectors; Pulse-height weighting technique; Time-of-flight method |
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Abstract |
In this work we explore for the first time the applicability of using gamma-ray imaging in neutron capture measurements to identify and suppress spatially localized background. For this aim, a pinhole gamma camera is assembled, tested and characterized in terms of energy and spatial performance. It consists of a monolithic CeBr3 scintillating crystal coupled to a position-sensitive photomultiplier and readout through an integrated circuit AMIC2GR. The pinhole collimator is a massive carven block of lead. A series of dedicated measurements with calibrated sources and with a neutron beam incident on a Au-197 sample have been carried out at n_TOF, achieving an enhancement of a factor of two in the signal-to-background ratio when selecting only those events coming from the direction of the sample. |
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Address |
[Perez Magan, D. L.; Caballero, L.; Domingo-Pardo, C.; Agramunt-Ros, J.; Albiol, F.; Tarifeno-Saldivia, A.] Univ Valencia, CSIC, IFIC, E-46071 Valencia, Spain, Email: domingo@ific.uv.es |
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Publisher |
Elsevier Science Bv |
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English |
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Original Title |
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Series Editor |
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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 |
0168-9002 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000374661600015 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
2665 |
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Author |
Albiol, F.; Corbi, A.; Albiol, A. |
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Title |
Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D Reconstruction |
Type |
Journal Article |
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Year |
2016 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
IEEE Trans. Med. Imaging |
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Volume |
35 |
Issue |
8 |
Pages |
1952-1961 |
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Keywords |
3D reconstruction; camera system; geometric calibration; visible fiducials; X-ray imaging |
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Abstract |
We present a methodology to recover the geometrical calibration of conventional X-ray settings with the help of an ordinary video camera and visible fiducials that are present in the scene. After calibration, equivalent points of interest can be easily identifiable with the help of the epipolar geometry. The same procedure also allows the measurement of real anatomic lengths and angles and obtains accurate 3D locations from image points. Our approach completely eliminates the need for X-ray-opaque reference marks (and necessary supporting frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session have been carried out. The results show that it is possible to identify common points with a proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a millimetric level of precision. The presented approach is simple and compatible with both current and legacy widespread diagnostic X-ray imaging deployments and it can represent a good and inexpensive alternative to other radiological modalities like CT. |
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Address |
[Albiol, Francisco; Corbi, Alberto] Univ Valencia, Consejo Super Invest Cient, Inst Fis Corpuscular IFIC, Paterna 46980, Spain, Email: kiko@ific.uv.es; |
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Publisher |
Ieee-Inst Electrical Electronics Engineers Inc |
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 |
0278-0062 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000381436000016 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
2781 |
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Author |
Albiol, F.; Corbi, A.; Albiol, A. |
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Title |
Densitometric Radiographic Imaging With Contour Sensors |
Type |
Journal Article |
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Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
IEEE Access |
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Volume |
7 |
Issue |
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Pages |
18902-18914 |
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Keywords |
Conventional X-ray imaging; contour data; densitometric images; dynamic range; depth information |
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Abstract |
We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D-S) or bone-enhanced (D'(B)) figures. In the D-S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D-B' type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination. |
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Address |
[Albiol, Francisco; Corbi, Alberto] CSIC, Inst Fis Corpuscular, Paterna 46980, Spain, Email: kiko@ific.uv.es |
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Thesis |
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Publisher |
Ieee-Inst Electrical Electronics Engineers Inc |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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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 |
2169-3536 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000459591800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3920 |
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Author |
Caballero, L.; Albiol, F.; Corbi Bellot, A.; Domingo-Pardo, C.; Leganes Nieto, J.L.; Agramunt Ros, J.; Contreras, P.; Monserrate, M.; Olleros Rodriguez, P.; Perez Magan, D.L. |
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Title |
Gamma-ray imaging system for real-time measurements in nuclear waste characterisation |
Type |
Journal Article |
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Year |
2018 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
13 |
Issue |
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Pages |
P03016 - 23pp |
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Keywords |
Inspection with gamma rays; Radiation monitoring |
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Abstract |
Acompact, portable and large field-of-viewgamma camera that is able to identify, locate and quantify gamma-ray emitting radioisotopes in real-time has been developed. The device delivers spectroscopic and imaging capabilities that enable its use it in a variety of nuclear waste characterisation scenarios, such as radioactivity monitoring in nuclear power plants and more specifically for the decommissioning of nuclear facilities. The technical development of this apparatus and some examples of its application in field measurements are reported in this article. The performance of the presented gamma-camera is also benchmarked against other conventional techniques. |
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Address |
[Caballero, L.] Univ Valencia, CSIC, Inst Fis Corpuscular, C Catedrat Jose Beltran 2, E-46980 Paterna, Spain, Email: Luis.Caballero@ific.uv.es |
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Publisher |
Iop Publishing Ltd |
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English |
Summary Language |
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Edition |
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ISSN |
1748-0221 |
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Expedition |
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Conference |
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Notes |
WOS:000428146300006 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3540 |
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Author |
Real, D.; Calvo, D.; Zornoza, J.D.; Manzaneda, M.; Gozzini, R.; Ricolfe-Viala, C.; Lajara, R.; Albiol, F. |
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Title |
Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection |
Type |
Journal Article |
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Year |
2024 |
Publication |
Sensors |
Abbreviated Journal |
Sensors |
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Volume |
24 |
Issue |
7 |
Pages |
2084 - 12pp |
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Keywords |
time-to-digital converters; neutrino telescopes; silicon photomultipliers; dark noise rate filtering |
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Abstract |
Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tube signals serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering the Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as a trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method, is presented. |
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Address |
[Real, Diego; Calvo, David; Zornoza, Juan de Dios; Manzaneda, Mario; Gozzini, Rebecca; Albiol, Francisco] CSIC Univ Valencia, IFIC Inst Fis Corpuscular, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: real@ific.uv.es; |
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Publisher |
Mdpi |
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English |
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Notes |
WOS:001201226600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6063 |
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Author |
Tortajada, S.; Albiol, F.; Caballero, L.; Albiol, A.; Leganes-Nieto, J.L. |
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Title |
A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization |
Type |
Journal Article |
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Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
13 |
Issue |
1 |
Pages |
12284 - 10pp |
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Keywords |
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Abstract |
One of the main activities of the nuclear industry is the characterisation of radioactive waste based on the detection of gamma radiation. Large volumes of radioactive waste are classified according to their average activity, but often the radioactivity exceeds the maximum allowed by regulators in specific parts of the bulk. In addition, the detection of the radiation is currently based on static detection systems where the geometry of the bulk is fixed and well known. Furthermore, these systems are not portable and depend on the transport of waste to the places where the detection systems are located. However, there are situations where the geometry varies and where moving waste is complex. This is especially true in compromised situations.We present a new model for nuclear waste management based on a portable and geometry-independent tomographic system for three-dimensional image reconstruction for gamma radiation detection. The system relies on a combination of a gamma radiation camera and a visible camera that allows to visualise radioactivity using augmented reality and artificial computer vision techniques. This novel tomographic system has the potential to be a disruptive innovation in the nuclear industry for nuclear waste management. |
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Address |
[Tortajada, Salvador; Albiol, Francisco; Caballero, Luis] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46980 Paterna Valencia, Spain, Email: s.tortajada@ific.uv.es |
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Publisher |
Nature Portfolio |
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English |
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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 |
2045-2322 |
ISBN |
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Conference |
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Notes |
WOS:001041587900052 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5612 |
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Author |
Albiol, A.; Albiol, F.; Paredes, R.; Plasencia-Martinez, J.M.; Blanco Barrio, A.; Garcia Santos, J.M.; Tortajada, S.; Gonzalez Montano, V.M.; Rodriguez Godoy, C.E.; Fernandez Gomez, S.; Oliver-Garcia, E.; de la Iglesia Vaya, M.; Marquez Perez, F.L.; Rayo Madrid, J.I. |
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Title |
A comparison of Covid-19 early detection between convolutional neural networks and radiologists |
Type |
Journal Article |
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Year |
2022 |
Publication |
Insights into Imaging |
Abbreviated Journal |
Insights Imaging |
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Volume |
13 |
Issue |
1 |
Pages |
122 - 12pp |
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Keywords |
Deep learning; Covid-19; Radiology |
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Abstract |
Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19. |
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Address |
[Albiol, Alberto] Univ Politecn Valencia, iTeam Inst, ETSI Telecomunicac, Camino Vera S-N, Valencia 46022, Spain, Email: alalbiol@iteam.upv.es |
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Corporate Author |
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Thesis |
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Publisher |
Springer |
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 |
1869-4101 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000832727200003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5302 |
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