Abstract
In the obtention of medical images, the patients’ movement can modify the identification of the body components in an image. The combination of imaging techniques may not always be a ...solution to improve the imaging quality; therefore, an artifact analysis is commonly required prior to applying an imaging procedure in patients. In this work, we systematically evaluated the movements’ artifacts caused by the patients’ breathing during the images acquisition and their impact on the fusion of SPECT and CT modalities. We used a specific phantom placed on a platform to emulate the respiratory movement, finding artifacts not appreciable under the standard condition used to obtain the SPECT images due to its low spatial resolution. The artifacts produced a deformation of elements on the images. Therefore, image processing was necessary to identify the registration accuracy with SPECT and CT modalities in two states (phantom at rest and for a phantom with simulated respiratory movements). A systematic difference was obtained for the first case (11.7 mm), and a range of (7.4 mm to 16.1 mm) for the second one. For the volumes’ evaluation, the optimal threshold value for CT was 0.40 and for SPECT was 0.25, giving a rapid solution to reduce the artifacts’ impact on medical images.
Central nervous system (CNS) diseases represent an extreme burden with significant social and economic costs. A common link in most brain pathologies is the appearance of inflammatory components that ...can jeopardize the stability of the implanted biomaterials and the effectiveness of therapies. Different silk fibroin scaffolds have been used in applications related to CNS disorders. Although some studies have analyzed the degradability of silk fibroin in non-cerebral tissues (almost exclusively upon non-inflammatory conditions), the stability of silk hydrogel scaffolds in the inflammatory nervous system has not been studied in depth. In this study, the stability of silk fibroin hydrogels exposed to different neuroinflammatory contexts has been explored using an in vitro microglial cell culture and two in vivo pathological models of cerebral stroke and Alzheimer's disease. This biomaterial was relatively stable and did not show signs of extensive degradation across time after implantation and during two weeks of in vivo analysis. This finding contrasted with the rapid degradation observed under the same in vivo conditions for other natural materials such as collagen. Our results support the suitability of silk fibroin hydrogels for intracerebral applications and highlight the potentiality of this vehicle for the release of molecules and cells for acute and chronic treatments in cerebral pathologies.
The development of a breast phantom with realistic features that is simple and cheap to produce continues to be a challenge. Most commercial phantoms are expensive and unrealistic. This work proposes ...a breast phantom constructed with PMMA material and acrylic resin, considered as tissue-equivalent materials, to represent a breast of average density in a prototype mammography/tomosynthesis unit, and was designed to contain structures that represent mammary lesions such as microcalcifications, regular and irregular masses. The materials used for the construction are accessible to buy and easy to handle.
•A breast phantom was made with PMMA material and acrylic resin.•The mammographic image allowed to identify the internal structures of the breast phantom.•The breast phantom with internal structures was constructed for use in DR mammography.
Several curricular initiatives have been developed to improve the acquisition of research competencies by Health Science students.
To know how students self-perceived of whether their participation ...in the XIV National Research Congress for Undergraduate Students of Health Sciences had helped them in the acquisition of 36 research-related transferable competencies (TCs) common to Health Science degrees.
A survey design (Cronbach's alpha = 0.924), using a self-administered questionnaire, was conducted among undergraduate students who voluntarily participated in the Congress. Data analysis was performed using SPSS 25 and Statgraphics 19. Statistical significance was considered for P < 0.05.
Eighty-one students from 12 Health Science degree programs responded. Key findings are presented in a structured manner, using a Likert-5 scale. Twenty-five of the competencies surveyed obtained an average ≥ 4 highlighting: “Critically evaluate and know how to use sources of clinical and biomedical information to obtain, organize, interpret, and communicate scientific and health information”; “To be able to formulate hypotheses, collect and critically evaluate information for problem solving, following the scientific method”, “Critical analysis and research” and “Communicate effectively and clearly, orally and in writing with other professionals”. Significance was found in 15 competencies. The development of the competencies “Teamwork”, “Critical reasoning” and “Analysis and synthesis abilities” was considered to be of greater "personal utility" by the respondents.
Participation in this event contributed to the development of research-related TCs, critical analysis and information management and communication, especially in relation to learning the sources of clinical and biomedical information, to know, following the scientific method, how to formulate hypotheses that allow students to solve problems in their professional activity. The experience was significantly influenced by the respondents’ year, the type of participation in the event and the gender of the students. Limitations and suggestions regarding future research are discussed to encourage further exploration of the topic.
In many countries, there is an interest in determining the location of the women with the highest breast density. This investigation is important for optimize screening for breast cancer for women ...with dense breasts as other imaging modalities since 2D mammography is not very efficient on this type of breast. The objective of this study was to evaluate the variations in breast density in Brazilian women of different regions of Brazil. The mammographic images were taken from four regions of Brazil. The images, in the cranial caudal (CC) projection, were separated into intervals of compressed breast thickness (CBT) and patient age and were analysed by the software VolparaDensity, where volumetric breast density (VBD) calculations were performed. For each interval, null hypothesis tests for the mean difference between the VBD from the four regions of Brazil were performed. The paired tests indicated that there was a significant difference in the VBD of the women in the different regions of Brazil, with variations from 11.05% to 36.73%. Higher VBD was observed for women living in the Southeast region, followed by the Midwest, Northeast, and North regions. The Brazilian IBGE data show that the most urbanised region in Brazil is the Southeast, which coincides with the second highest rate of breast cancer in Brazil, according to the Brazilian National Cancer Institute (INCA). It is also known that breast cancer is strongly related to breast density; therefore, the results of this work support the data presented by federal agencies demonstrating that women living in the most urbanised region of Brazil (e.g., Southeast) present the highest breast density.
Mammography, Breast density, Breast cancer, Specific screening.
Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to ...compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.
Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.
The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval CI = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.
The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.
Purpose
To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.
Methods ...and materials
A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis.
Results
Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (− 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > − 0.05) for any threshold except at the extreme AI score of 9.
Conclusion
It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload.
Key Points
• There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer.
• The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists’ breast cancer detection performance.
• When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.
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