Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study.

BACKGROUND: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mammography. METHODS: In this randomised, controlled, population-based trial, women aged 40-80 years eligible for mammography screening (including general screening with 1·5-2-year intervals and annual screening for those with moderate hereditary risk of breast cancer or a history of breast cancer) at four screening sites in Sweden were informed about the study as part of the screening invitation. Those who did not opt out were randomly allocated (1:1) to AI-supported screening (intervention group) or standard double reading without AI (control group). Screening examinations were automatically randomised by the Picture Archive and Communications System with a pseudo-random number generator after image acquisition. The participants and the radiographers acquiring the screening examinations, but not the radiologists reading the screening examinations, were masked to study group allocation. The AI system (Transpara version 1.7.0) provided an examination-based malignancy risk score on a 10-level scale that was used to triage screening examinations to single reading (score 1-9) or double reading (score 10), with AI risk scores (for all examinations) and computer-aided detection marks (for examinations with risk score 8-10) available to the radiologists doing the screen reading. Here we report the prespecified clinical safety analysis, to be done after 80 000 women were enrolled, to assess the secondary outcome measures of early screening performance (cancer detection rate, recall rate, false positive rate, positive predictive value [PPV] of recall, and type of cancer detected [invasive or in situ]) and screen-reading workload. Analyses were done in the modified intention-to-treat population (ie, all women randomly assigned to a group with one complete screening examination, excluding women recalled due to enlarged lymph nodes diagnosed with lymphoma). The lowest acceptable limit for safety in the intervention group was a cancer detection rate of more than 3 per 1000 participants screened. The trial is registered with ClinicalTrials.gov, NCT04838756, and is closed to accrual; follow-up is ongoing to assess the primary endpoint of the trial, interval cancer rate. FINDINGS: Between April 12, 2021, and July 28, 2022, 80 033 women were randomly assigned to AI-supported screening (n=40 003) or double reading without AI (n=40 030). 13 women were excluded from the analysis. The median age was 54·0 years (IQR 46·7-63·9). Race and ethnicity data were not collected. AI-supported screening among 39 996 participants resulted in 244 screen-detected cancers, 861 recalls, and a total of 46 345 screen readings. Standard screening among 40 024 participants resulted in 203 screen-detected cancers, 817 recalls, and a total of 83 231 screen readings. Cancer detection rates were 6·1 (95% CI 5·4-6·9) per 1000 screened participants in the intervention group, above the lowest acceptable limit for safety, and 5·1 (4·4-5·8) per 1000 in the control group-a ratio of 1·2 (95% CI 1·0-1·5; p=0·052). Recall rates were 2·2% (95% CI 2·0-2·3) in the intervention group and 2·0% (1·9-2·2) in the control group. The false positive rate was 1·5% (95% CI 1·4-1·7) in both groups. The PPV of recall was 28·3% (95% CI 25·3-31·5) in the intervention group and 24·8% (21·9-28·0) in the control group. In the intervention group, 184 (75%) of 244 cancers detected were invasive and 60 (25%) were in situ; in the control group, 165 (81%) of 203 cancers were invasive and 38 (19%) were in situ. The screen-reading workload was reduced by 44·3% using AI. INTERPRETATION: AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up. FUNDING: Swedish Cancer Society, Confederation of Regional Cancer Centres, and the Swedish governmental funding for clinical research (ALF).

Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists.

OBJECTIVES: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession. METHODS: We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI's impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach. RESULTS: Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making. CONCLUSIONS: Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

Good manufacturing procedure production of [18 F]SynVesT-1, a radioligand for in vivo positron emission tomography imaging of synaptic vesicle glycoprotein 2A.

[18 F]SynVesT-1 (also known as [18 F]SDM-8 or [18 F]MNI-1126) is a potent and selective synaptic vesicle glycoprotein 2 (SV2A) positron emission tomography (PET) imaging agent. In order to fulfill the increasing clinical demand of an 18 F-labeled SV2A PET ligand, we have developed a fully automated procedure to provide a sterile and pyrogen-free good manufacturing procedure (GMP)-compliant product of [18 F]SynVesT-1 suitable for clinical studies in humans. [18 F]SynVesT-1 is synthesized via a rapid copper-mediated radiofluorination protocol. The procedure was developed and established on a commercially available module, TracerMaker (ScanSys Laboratorieteknik ApS, Copenhagen, Denmark), a synthesis platform originally developed to conduct carbon-11 radiochemistry. From ~130 GBq (end-of-bombardment), our newly developed procedure enabled us to prepare [18 F]SynVesT-1 in an isolated radioactivity yield of 14,220 ± 800 MBq (n = 3), which corresponds to a radiochemical yield (RCY) of 19.5 ± 0.5%. The radiochemical purity (RCP) and enantiomeric purity of each of the final formulated batches exceeded 98%. The overall synthesis time was 90 min and the molar activity was 330 ± 60 GBq/µmol (8.9 ± 1.6 Ci/µmol). The produced [18 F]SynVesT-1 was stable over 8 h at room temperature and is suitable for in vivo PET imaging studies in human subjects.

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients.

Objectives: To update the sets of patient-centric outcomes measures ("standard-sets") developed by the not-for-profit organization ICHOM to become more readily applicable in patients with multimorbidity and to facilitate their implementation in health information systems. To that end we set out to (i) harmonize measures previously defined separately for different conditions, (ii) create clinical information models from the measures, and (iii) restructure the annotation to make the sets machine-readable. Materials and Methods: First, we harmonized the semantic meaning of individual measures across all the 28 standard-sets published to date, in a harmonized measure repository. Second, measures corresponding to four conditions (Breast cancer, Cataracts, Inflammatory bowel disease and Heart failure) were expressed as logical models and mapped to reference terminologies in a pilot study. Results: The harmonization of semantic meaning resulted in a consolidation of measures used across the standard-sets by 15%, from 3,178 to 2,712. These were all converted into a machine-readable format. 61% of the measures in the 4 pilot sets were bound to existing concepts in either SNOMED CT or LOINC. Discussion: The harmonization of ICHOM measures across conditions is expected to increase the applicability of ICHOM standard-sets to multi-morbid patients, as well as facilitate their implementation in health information systems. Conclusion: Harmonizing the ICHOM measures and making them machine-readable is expected to expedite the global adoption of systematic and interoperable outcomes measurement. In turn, we hope that the improved transparency on health outcomes that follows will let health systems across the globe learn from each other to the ultimate benefit of patients.

Questionnaire-based evaluation of mobile phone interference with medical-electrical equipment in Swedish hospitals.

National recommendations in Sweden recommend a safety distance of 3 meter (m) between mobile phones and medical-electrical (ME) equipment in hospitals. A questionnaire was used to investigate how often mobile phones were reported to interfere with ME products in clinical practice across Sweden. The results confirmed that ME equipment can be affected by mobile phone use but, the risk of the patient's outcome being affected were minimal; no cases were identified which led to injury or death. In conclusion, the results support recommendations for a general safety distance of 0.5 m between mobile phones and ME equipment in care environments.

Quantifying Geographic Variation in Health Care Outcomes in the United States before and after Risk-Adjustment.

BACKGROUND: Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment. METHODS AND FINDINGS: We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R2 between 18-64% of variability in mortality outcomes, 3-39% of variability in patient safety outcomes, and 22-70% of variability in prevention outcomes. CONCLUSION: The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system.

Use of 13 disease registries in 5 countries demonstrates the potential to use outcome data to improve health care's value.

As health care systems worldwide struggle with rising costs, a consensus is emerging to refocus reform efforts on value, as determined by the evaluation of patient outcomes relative to costs. One method of using outcome data to improve health care value is the disease registry. An international study of thirteen registries in five countries (Australia, Denmark, Sweden, the United Kingdom, and the United States) suggests that by making outcome data transparent to both practitioners and the public, well-managed registries enable medical professionals to engage in continuous learning and to identify and share best clinical practices. The apparent result: improved health outcomes, often at lower cost. For example, we calculate that if the United States had a registry for hip replacement surgery comparable to one in Sweden that enabled reductions in the rates at which these surgeries are performed a second time to replace or repair hip prostheses, the United States would avoid $2 billion of an expected $24 billion in total costs for these surgeries in 2015.

Influences of sex and activity level on physiological changes in individual adult sockeye salmon during rapid senescence.

A noninvasive biopsy protocol was used to sample plasma and gill tissue in individual sockeye salmon (Oncorhynchus nerka) during the critical life stage associated with spawning-arrival at a spawning channel through senescence to death several days later. Our main objective was to characterize the physiological changes associated with rapid senescence in terms of the physiological stress/cortisol hypersecretion model and the energy exhaustion model. Salmon lived an average of 5 d in the spawning channel, during which time there were three major physiological trends that were independent of sexual status: a large increase in plasma indicators of stress and exercise (i.e., lactate and cortisol), a decrease in the major plasma ions (i.e., Cl(-) and Na(+)) and osmolality, and a decrease in gross somatic energy reserves. Contrary to a generalized stress response, plasma glucose decreased in approximately 2/3 of the fish after arrival, as opposed to increasing. Furthermore, plasma cortisol levels at spawning-ground arrival were not correlated with the degree of ionoregulatory changes during rapid senescence. One mechanism of mortality in some fish may involve the exhaustion of energy reserves, resulting in the inability to mobilize plasma glucose. Sex had a significant modulating effect on the degree of physiological change. Females exhibited a greater magnitude of change for gross somatic energy, osmolality, and plasma concentrations of Cl(-), Na(+), cortisol, testosterone, 11-ketotestosterone, 17,20beta-progesterone, and estradiol. The activity level of an individual on the spawning grounds appeared to influence the degree of some physiological changes during senescence. For example, males that received a greater frequency of attacks exhibited larger net decreases in plasma 11-ketotestosterone while on the spawning grounds. These results suggest that rapid senescence on spawning grounds is influenced by multiple physiological processes and perhaps behavior. This study provides some of the first data to look at sex differences in senescence in Pacific salmon.