| |
Records |
Links |
|
Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
|
| |
Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
|
| |
Volume |
3 |
Issue |
3 |
Pages  |
e200265 - 15pp |
|
| |
Keywords |
|
|
| |
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. |
|
| |
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 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Amer Medical Assoc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2574-3805 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000519249800002 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
4683 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Albiol, F.; Corbi, A.; Albiol, A. |
|
| |
Title |
Densitometric Radiographic Imaging With Contour Sensors |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
IEEE Access |
|
| |
Volume |
7 |
Issue |
|
Pages |
18902-18914 |
|
| |
Keywords |
Conventional X-ray imaging; contour data; densitometric images; dynamic range; depth information |
|
| |
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. |
|
| |
Address |
[Albiol, Francisco; Corbi, Alberto] CSIC, Inst Fis Corpuscular, Paterna 46980, Spain, Email: kiko@ific.uv.es |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Ieee-Inst Electrical Electronics Engineers Inc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2169-3536 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000459591800001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
no |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
3920 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Tortajada, S.; Albiol, F.; Caballero, L.; Albiol, A.; Leganes-Nieto, J.L. |
|
| |
Title |
A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
|
| |
Volume |
13 |
Issue |
1 |
Pages |
12284 - 10pp |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
[Tortajada, Salvador; Albiol, Francisco; Caballero, Luis] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46980 Paterna Valencia, Spain, Email: s.tortajada@ific.uv.es |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Nature Portfolio |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2045-2322 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:001041587900052 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
no |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5612 |
|
| Permanent link to this record |
| |
|
| |
|
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. |
|
| |
Title |
Gamma-ray imaging system for real-time measurements in nuclear waste characterisation |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
|
| |
Volume |
13 |
Issue |
|
Pages |
P03016 - 23pp |
|
| |
Keywords |
Inspection with gamma rays; Radiation monitoring |
|
| |
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. |
|
| |
Address |
[Caballero, L.] Univ Valencia, CSIC, Inst Fis Corpuscular, C Catedrat Jose Beltran 2, E-46980 Paterna, Spain, Email: Luis.Caballero@ific.uv.es |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Iop Publishing Ltd |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1748-0221 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000428146300006 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
no |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
3540 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Real, D.; Calvo, D.; Zornoza, J.D.; Manzaneda, M.; Gozzini, R.; Ricolfe-Viala, C.; Lajara, R.; Albiol, F. |
|
| |
Title |
Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection |
Type |
Journal Article |
| |
Year |
2024 |
Publication |
Sensors |
Abbreviated Journal |
Sensors |
|
| |
Volume |
24 |
Issue |
7 |
Pages |
2084 - 12pp |
|
| |
Keywords |
time-to-digital converters; neutrino telescopes; silicon photomultipliers; dark noise rate filtering |
|
| |
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. |
|
| |
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; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Mdpi |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:001201226600001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
no |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
6063 |
|
| Permanent link to this record |
