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General General

Developing AI enabled sensors and decision support for military operators in the field.

In Journal of science and medicine in sport ; h5-index 59.0

Wearable sensors enable down range data collection of physiological and cognitive performance of the warfighter. However, autonomous teams may find the sensor data impractical to interpret and hence influence real-time decisions without the support of subject matter experts. Decision support tools can reduce the burden of interpreting physiological data in the field and incorporate a systems perspective where noisy field data can contain useful additional signals. We present a methodology of how artificial intelligence can be used for modeling human performance with decision-making to achieve actionable decision support. We provide a framework for systems design and advancing from the laboratory to real world environments. The result is a validated measure of down-range human performance with a low burden of operation.

Russell B K, McGeown J, Beard B L

2023-Mar-05

Artificial Intelligence, Cognition performance, Decision Support, Remote physiological monitoring

Cardiology Cardiology

Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study.

In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

AIMS : Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers, and further, to compare the results to manual measurements.

METHODS AND RESULTS : Two test-retest datasets (n=40 and n=32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by two different echocardiographers at each center. For each dataset, four readers measured GLS in both recordings using a semi-automatic method to construct test-retest inter-reader and intra-reader scenarios. Agreement, mean absolute difference and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in three cardiac cycles was assessed by two readers and AI. Test-retest variability was lower with AI compared to inter-reader scenarios (dataset I: MDC 3.7 vs 5.5, mean absolute difference 1.4 vs 2.1; dataset II: MDC 3.9 vs 5.2, mean absolute difference 1.6 vs 1.9, all p<0.05). There was bias in GLS measurements in 13 of 24 test-retest inter-reader scenarios (largest bias 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the two readers, respectively. Processing time for analyses of GLS by the AI method was 7.9±2.8 seconds.

CONCLUSION : A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest datasets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.

Salte Ivar M, Østvik Andreas, Olaisen Sindre H, Karlsen Sigve, Dahlslett Thomas, Smistad Erik, Eriksen-Volnes Torfinn Kirknes, Brunvand Harald, Haugaa Kristina H, Edvardsen Thor, Dalen Håvard, Lovstakken Lasse, Grenne Bjørnar

2023-Mar-16

Artificial intelligence, Echocardiography, Left ventricular function, Machine learning, Repeatability, Reproducibility, Strain

General General

Cryo-electron microscopy reveals the structure of the nuclear pore complex.

In Journal of molecular biology ; h5-index 65.0

The nuclear pore complex (NPC) is a giant protein assembly that penetrates the double layers of the nuclear membrane. The overall structure of the NPC has approximately eightfold symmetry and is formed by approximately 30 nucleoporins. The great size and complexity of the NPC have hindered the study of its structure for many years until recent breakthroughs were achieved by integrating the latest high-resolution cryo-electron microscopy (cryo-EM), the emerging artificial intelligence-based modeling and all other available structural information from crystallography and mass spectrometry. Here, we review our latest knowledge of the NPC architecture and the history of its structural study from in vitro to in situ with progressively improved resolutions by cryo-EM, with a particular focus on the latest subnanometer-resolution structural studies. The future directions for structural studies of NPCs are also discussed.

Tai Linhua, Yin Guoliang, Sun Fei, Zhu Yun

2023-Mar-16

Surgery Surgery

Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

In Magnetic resonance imaging

INTRODUCTION : The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification.

METHODS : 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM).

RESULTS : SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input.

CONCLUSIONS : ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Nai Ying-Hwey, Cheong Dennis Lai Hong, Roy Sharmili, Kok Trina, Stephenson Mary C, Schaefferkoetter Josh, Totman John J, Conti Maurizio, Eriksson Lars, Robins Edward G, Wang Ziting, Chua Wynne Yuru, Ang Bertrand Wei Leng, Singha Arvind Kumar, Thamboo Thomas Paulraj, Chiong Edmund, Reilhac Anthonin

2023-Mar-16

Gleason score (GS), Machine learning (ML), Multiparametric magnetic resonance imaging (mpMRI), Positron emission tomography (PET), Quantitative parameters, Radiomics

Surgery Surgery

Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk Calculator.

In The Journal of arthroplasty ; h5-index 65.0

BACKGROUND : Many risk factors have been described for periprosthetic femur fracture (PPFFx) following total hip arthroplasty (THA), yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to develop a high-dimensional, patient-specific risk-stratification nomogram that allows dynamic risk modification based on operative decisions.

METHODS : We evaluated 16,696 primary non-oncologic THAs performed between 1998 and 2018. During mean 6-year follow-up, 558 patients (3.3%) sustained PPFFx. Patients were characterized by individual natural language processing-assisted chart review on non-modifiable factors (demographics, THA indication, comorbidities), and modifiable operative decisions (femoral fixation [cemented/uncemented], surgical approach [direct anterior, lateral, posterior], implant type [collared/collarless]). Multivariable Cox regression models and nomograms were developed with PPFFx as a binary outcome at 90-days, 1-year, and 5-years postoperatively.

RESULTS : Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4-18% at 90-days, 0.4-20% at 1-year, and 0.5-25% at 5-years. Among 18 evaluated patient factors, 7 were retained in multivariable analyses. The 4 significant non-modifiable factors included: women (Hazard Ratio (HR)=1.6), older age (HR=1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR=1.7), and indication for surgery other than osteoarthritis (HR=2.2 for fracture, HR=1.8 for inflammatory arthritis, HR=1.7 for osteonecrosis). The 3 modifiable surgical factors were included: uncemented femoral fixation (HR=2.5), collarless femoral implants (HR=1.3), and surgical approach other than direct anterior (lateral HR=2.9, posterior HR=1.9).

CONCLUSION : This patient-specific PPFFx risk calculator demonstrated a wide-ranging risk based on comorbid profile and enables surgeons to quantify risk mitigation based on operative decisions.

Wyles Cody C, Maradit-Kremers Hilal, Fruth Kristin M, Larson Dirk R, Khosravi Bardia, Rouzrokh Pouria, Johnson Quinn J, Berry Daniel J, Sierra Rafael J, Taunton Michael J, Abdel Matthew P

2023-Mar-16

Patient-Specific, Periprosthetic Femur Fracture, Prognosis, Risk Calculator, Risk Modification, Total Hip Arthroplasty

oncology Oncology

Trends and Opportunities in Computable Clinical Phenotyping: A Scoping Review.

In Journal of biomedical informatics ; h5-index 55.0

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1% (N=121) of all studies, and International Classification of Diseases codes were heavily used in 55.4% (N=77) of all studies, however, only 25.9% (N=36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.

He Ting, Belouali Anas, Patricoski Jessica, Lehmann Harold, Ball Robert, Anagnostou Valsamo, Kreimeyer Kory, Botsis Taxiarchis

2023-Mar-16

Cohort Selection, Computable Phenotype, Precision Medicine, Precision Oncology