Real-Time Echocardiography Guidance for Optimized Apical Standard Views

Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the transducer such that the 2-D imaging plane properly aligns with the heart for standard measurement views and is thus dependent on the operator's skills. We propose a deep learning tool that suggests transducer movements to help users navigate toward the required standard views while scanning. The tool can simplify echocardiography for less experienced users and improve image standardization for more experienced users. Training data were generated by slicing 3-D ultrasound volumes, which permits simulation of the movements of a 2-D transducer. Neural networks were further trained to calculate the transducer position in a regression fashion. The method was validated and tested on 2-D images from several data sets representative of a prospective clinical setting. The method proposed the adequate transducer movement 75% of the time when averaging over all degrees of freedom and 95% of the time when considering transducer rotation solely. Real-time application examples illustrate the direct relation between the transducer movements, the ultrasound image and the provided feedback.publishedVersio

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

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 to compare the results to manual measurements. Methods: Two test-retest data sets (n = 40 and n = 32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader 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 3 cardiac cycles was assessed by 2 readers and AI. Results: Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC = 3.7 vs 5.5, mean absolute difference = 1.4 vs 2.1, respectively; data set II: MDC = 3.9 vs 5.2, mean absolute difference = 1.6 vs 1.9, respectively; all P < .05). There was bias in GLS measurements in 13 of 24 test-retest interreader 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 2 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 data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.publishedVersio

Tverrfaglig poliklinisk behandling av sykelig overvekt

Bakgrunn: Overvektsepidemien skrider frem og rammer stadig flere over hele verden. Verdens helseorganisasjon har innsett alvoret og laget en plan for å forebygge livsstilssykdommer. Det norske Helse- og omsorgsdepartementet har pålagt helseregionene å etablere behandlingstilbud for pasienter med helseproblemer som følge av uttalt overvekt og fedme, og Senter for sykelig overvekt ved Universitetssykehuset i Nord-Norge, Tromsø, er opprettet som respons til dette. Formålet med oppgaven: - Å skaffe en oversikt over hvorvidt kriteriene for henvisning er oppfylt i det første pasientmaterialet fra Senter for sykelig overvekt. - Å kartlegge forekomst av komorbiditet i deltakergruppen og vurdere effekt av tiltak hos oppfølgingsgruppen. Grunnleggende framgangsmåte: Det ble registrert antropometriske mål og blodprøver hos 295 pasienter som ble tatt inn til primærvurdering ved Senter for sykelig overvekt i registreringsperioden. Nye blodprøver ble tatt etter 6-12 måneder oppfølging og nye vektmålinger ble utført etter 3, 6 og 12 måneder for 42 pasienter som fikk tverrfaglig poliklinisk behandling. Viktigste funn: I hele deltakergruppen (n=295) var gjennomsnittlig BMI 42.8 ved første konsultasjon. 69.2% av deltakerne hadde BMI over 40 og av disse hadde 53.9% kjent komorbiditet. 6.1% hadde BMI under 35. Oppfølgingsgruppen (n=42) oppnådde et gjennomsnittlig vekttap på 15.1 kg etter tolv måneder (p=0.008) Dette tilsvarer en reduksjon i gjennomsnittlig vekt på 12.2%. Det ble observert en reduksjon i gjennomsnittlig BMI fra 43.4 ved oppstart til 38.5 etter livsstilsintervensjon. Blodprøver etter behandling viste signifikant reduserte verdier for serum triglycerider, glukose og C-peptid. Hovedkonklusjoner: Konservativ behandling av sykelig overvekt i form av livsstilsintervensjon gir reduksjon i vekt og komorbiditet. Dette er også bekreftet i tidligere studier. Majoriteten av henviste pasienter fyller nasjonale kriterier for behandling av sykelig overvekt i spesialisthelsetjenesten. Bariatrisk kirurgi er i dag en etablert og dokumentert behandlingsform hos pasienter med sykelig overvekt, men ikke alle ønsker, eller fyller kriteriene for, bariatrisk kirurgi. Med sterkt økende overvekt blant befolkningen er det av vesentlig betydning å dokumentere effekt av livsstilsintervensjon

Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases

Aims - Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test–retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases. Methods and results - Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P Conclusion - The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts

Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence reduces time-consumption and inter-observer variability

Poster presentation at the international EACVI conference, 10.05.23 - 12.05.23, Barcelona, Spain