A novel blockchain-based architectural modal for healthcare data integrity: Covid19 screening laboratory use-case.

In this paper, we are proposing a blockchain-based architectural model to ensure the integrity of healthcare-sensitive data in an AI-based medical research context. In our approach, we will use the HL7 FHIR standardized data structure to ensure the interoperability of our approach with the existing hospital information systems (HIS). Indeed, structuring the data coming from several heterogeneous sources would enhance its quality. In addition, a standardised data structure would help establish a more accurate security and data protection model throughout the process of data collection cleansing and processing. Hence, we designed our architecture to be interoperable with all FHIR-based HISs to add a trust layer to the current medical research process. In this paper we are to achieve our goal, we will combine continua healthcare IoT architecture and Hyperledger fabric architecture. Our trust layer model is composed of four components: (1) an architecture that integrates with the HL7 FHIR data exchange framework, which extends an open protocol that supports efficient standards-based healthcare data exchange (2) a blockchain layer to support access control and auditing of FHIR health records that are stored in the data exchange network databases; (3) a distributed architecture consisting of multiple trusted nodes ensure privacy protection for health data; and (4) an application programming interface (API) will be available for use by the network.

Assessment of the relationship between regional wall motion abnormality score revealed by parametric imaging and the extent of LGE with CMR.

The aim of this study was to assess the relationship between left ventricular (LV) regional myocardial wall motion abnormality (WMA), revealed by visual interpretation of cardiac magnetic resonance (CMR) cine images together with the computed wall motion parametric image, and the transmural scar extent, as assessed by Late gadolinium Enhancement (LGE), in 40 patients. Each cine CMR short-axis loop was processed to compute a parametric image where each pixel represents the amplitude of the Hilbert transform of videointensity over time. Two expert radiologists blindly interpreted the cine CMR images in combination with the corresponding parametric image to assign a WMA score for each of the 16 myocardial sectors in which the LV myocardium was subdivided. Such score was compared per sector to the level of transmural scar extent obtained by LGE images. A total of 592 myocardial segments were analyzed. A significant decrease in regional wall motion was observed in sectors with LGE transmural hyperenhancement > 75% of tissue, as well as a correlation between parametric image amplitude and peak radial and circumferential strain, computed by feature tracking. The results showed a reduction in prediction error Lambda of WMA from LGE of 65%, and of LGE from WMA of 63%. In particular, the estimated probability of correct prediction of WMA from LGE was 76%, while that of LGE from WMA was 75%. The interpretation of myocardial viability by LGE images combined with the WMA information, derived from cine CMR and parametric images, could improve the clinical decision making process.

Investigation of serum proteome homeostasis during radiation therapy by a quantitative proteomics approach.

The purpose of the present study is to analyze the serum proteome of patients receiving Radiation Therapy (RT) at different stages of their treatment to discovery candidate biomarkers of the radiation-induced skin lesions and the molecular pathways underlying the radiation signatures. Six stages of RT treatment were monitored from patients treated because of brain cancer: before starting the treatment, during the treatment (four time points), and at 4 weeks from the last RT dose. Serum samples were analyzed by a proteomics approach based on the Data Independent Acquisition (DIA) mass spectrometry (MS). RT induced clear changes in the expression levels of 36 serum proteins. Among these, 25 proteins were down- or up-regulated significantly before the emergence of skin lesions. Some of these were still deregulated after the completion of the treatment. Few days before the appearance of the skin lesions, the levels of some proteins involved in the wound healing processes were down-regulated. The pathway analysis indicated that after partial body irradiation, the expression levels of proteins functionally involved in the acute inflammatory and immune response, lipoprotein process and blood coagulation, were deregulated.

Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal.

BACKGROUND: In breast ultrasound elastography, tissues displacements estimation is obtained through a technique that follows the evolution of tissues under stress. However, during the acquisition of B-mode images, tissue displacements are often contaminated with multiplicative noise caused by changes in the speckle pattern in the tissue. Thus, the application of monogenic signal technique on the B-mode image in order to estimate displacement tissue, result in a presence of amplified noise in the deformation tissue image, which severely obscures the useful information. In this paper, we propose a new method based on the monogenic features, that is to improve the old monogenic signal (OMS) technique by improving the filtering step, so that the use of an effective denoising technique is enough to ensure a good estimation of displacement tissue. Our proposed method is based on the use of a robust filtering technique combined with the monogenic model. METHODS: Two models of phantom elasticity are used in our test validation sold by CIRS company. In-vivo testing was also performed on the sets of clinical B-mode images to 20 patients including malignant breast tumors. Shrinkage wavelets has been used to eliminate the noise according to the threshold, then a guided filter is introduced to completely filter the image, the monogenic model is used after excerpting the image feature and estimating analytically the displacement tissue. RESULTS: Accurate and excellent displacement estimation for breast tissue was observed in proposed method results. By adapting our proposed approach to breast B-mode images, we have shown that it demonstrated a higher performance for displacement estimation; it gives better values in term of standard deviation, higher contrast to noise ratio, greater peak signal-to-noise ratio, excellent structural similarity and much faster speed than OMS and B-spline techniques. The results of the proposed model are encouraging, allowing quick and reliable estimations. CONCLUSION: Although the proposed approach is used in ultrasound domains, it has never been used in the estimation of the breast tissue displacement. In this context, our proposed approach could be a powerful diagnostic tool to be used in breast displacement estimation in ultrasound elastography.