Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI

BACKGROUND: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. METHODS: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). RESULTS: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). CONCLUSIONS: We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions

Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction

Background: Acute myocardial damage is common in severe COVID-19. Post-mortem studies have implicated microvascular thrombosis, with cardiovascular magnetic resonance (CMR) demonstrating a high prevalence of myocardial infarction and myocarditis-like scar. The microcirculatory sequelae are incompletely characterized. Perfusion CMR can quantify the stress myocardial blood flow (MBF) and identify its association with infarction and myocarditis. Objectives: To determine the impact of the severe hospitalized COVID-19 on global and regional myocardial perfusion in recovered patients. Methods: A case-control study of previously hospitalized, troponin-positive COVID-19 patients was undertaken. The results were compared with a propensity-matched, pre-COVID chest pain cohort (referred for clinical CMR; angiography subsequently demonstrating unobstructed coronary arteries) and 27 healthy volunteers (HV). The analysis used visual assessment for the regional perfusion defects and AI-based segmentation to derive the global and regional stress and rest MBF. Results: Ninety recovered post-COVID patients {median age 64 [interquartile range (IQR) 54-71] years, 83% male, 44% requiring the intensive care unit (ICU)} underwent adenosine-stress perfusion CMR at a median of 61 (IQR 29-146) days post-discharge. The mean left ventricular ejection fraction (LVEF) was 67 ± 10%; 10 (11%) with impaired LVEF. Fifty patients (56%) had late gadolinium enhancement (LGE); 15 (17%) had infarct-pattern, 31 (34%) had non-ischemic, and 4 (4.4%) had mixed pattern LGE. Thirty-two patients (36%) had adenosine-induced regional perfusion defects, 26 out of 32 with at least one segment without prior infarction. The global stress MBF in post-COVID patients was similar to the age-, sex- and co-morbidities of the matched controls (2.53 ± 0.77 vs. 2.52 ± 0.79 ml/g/min, p = 0.10), though lower than HV (3.00 ± 0.76 ml/g/min, p< 0.01). Conclusions: After severe hospitalized COVID-19 infection, patients who attended clinical ischemia testing had little evidence of significant microvascular disease at 2 months post-discharge. The high prevalence of regional inducible ischemia and/or infarction (nearly 40%) may suggest that occult coronary disease is an important putative mechanism for troponin elevation in this cohort. This should be considered hypothesis-generating for future studies which combine ischemia and anatomical assessment

Patterns of myocardial injury in recovered troponin-positive COVID-19 patients assessed by cardiovascular magnetic resonance

BACKGROUND: Troponin elevation is common in hospitalized COVID-19 patients, but underlying aetiologies are ill-defined. We used multi-parametric cardiovascular magnetic resonance (CMR) to assess myocardial injury in recovered COVID-19 patients. METHODS AND RESULTS: One hundred and forty-eight patients (64 ± 12 years, 70% male) with severe COVID-19 infection [all requiring hospital admission, 48 (32%) requiring ventilatory support] and troponin elevation discharged from six hospitals underwent convalescent CMR (including adenosine stress perfusion if indicated) at median 68 days. Left ventricular (LV) function was normal in 89% (ejection fraction 67% ± 11%). Late gadolinium enhancement and/or ischaemia was found in 54% (80/148). This comprised myocarditis-like scar in 26% (39/148), infarction and/or ischaemia in 22% (32/148) and dual pathology in 6% (9/148). Myocarditis-like injury was limited to three or less myocardial segments in 88% (35/40) of cases with no associated LV dysfunction; of these, 30% had active myocarditis. Myocardial infarction was found in 19% (28/148) and inducible ischaemia in 26% (20/76) of those undergoing stress perfusion (including 7 with both infarction and ischaemia). Of patients with ischaemic injury pattern, 66% (27/41) had no past history of coronary disease. There was no evidence of diffuse fibrosis or oedema in the remote myocardium (T1: COVID-19 patients 1033 ± 41 ms vs. matched controls 1028 ± 35 ms; T2: COVID-19 46 ± 3 ms vs. matched controls 47 ± 3 ms). CONCLUSIONS: During convalescence after severe COVID-19 infection with troponin elevation, myocarditis-like injury can be encountered, with limited extent and minimal functional consequence. In a proportion of patients, there is evidence of possible ongoing localized inflammation. A quarter of patients had ischaemic heart disease, of which two-thirds had no previous history. Whether these observed findings represent pre-existing clinically silent disease or de novo COVID-19-related changes remain undetermined. Diffuse oedema or fibrosis was not detected

Anti-Müllerian hormone (AMH) in the diagnosis of menstrual disturbance due to polycystic ovarian syndrome

Funding: funded by grants from the MRC, BBSRC, and NIHR and supported by the NIHR/Wellcome Trust Imperial Clinical Research Facility and Imperial Biomedical Research Centre. The Section of Endocrinology and Investigative Medicine is funded by grants from the MRC, BBSRC, and NIHR and is supported by the NIHR Biomedical Research Centre Funding Scheme.Introduction : Polycystic ovarian syndrome (PCOS) is a leading cause of female subfertility worldwide, however due to the heterogeneity of the disorder, the criteria for diagnosis remains subject to conjecture. In the present study, we evaluate the utility of serum Anti-Müllerian Hormone (AMH) in the diagnosis of menstrual disturbance due to PCOS. Method : Menstrual cycle length, serum AMH, gonadotropin and sex-hormone levels, total antral follicle count (AFC), body mass index (BMI) and ovarian morphology on ultrasound were analyzed in a cohort of 187 non-obese women, aged 18–35 years, screened for participation in a clinical trial of fertility treatment between 2013 and 2016 at a tertiary reproductive endocrine center. Results : Serum AMH was higher in women with menstrual disturbance when compared to those with regular cycles (65.6 vs. 34.8 pmol/L; P 60 pmol/L, in comparison to those with an AMH < 15 pmol/L. AMH better discriminated women with menstrual disturbance (area under ROC 0.77) from those with regular menstrual cycles than AFC (area under ROC 0.67), however the combination of the two markers increased discrimination than either measure alone (0.83; 95% CI 0.77–0.89). Serum AMH was higher in women with all three cardinal features of PCOS (menstrual disturbance, hyperandrogenism, polycystic ovarian morphology) when compared to women with none of these features (65.6 vs. 14.6 pmol/L; P < 0.0001). The odds of menstrual disturbance were increased by 10.7-fold (95% CI 2.4–47.1) in women with bilateral polycystic morphology ovaries than those with normal ovarian morphology. BMI was a stronger predictor of free androgen index (FAI) than either AMH or AFC. Conclusion : Serum AMH could serve as a useful biomarker to indicate the risk of menstrual disturbance due to PCOS. Women with higher AMH levels had increased rates of menstrual disturbance and an increased number of features of PCOS.Publisher PDFPeer reviewe

Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative

Background: requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly

Using kisspeptin to assess GnRH function in an unusual case of primary amenorrhoea

SUMMARY: Primary amenorrhoea is defined as the failure to commence menstruation by the age of 15 years, in the presence of normal secondary sexual development. The potential causes of primary amenorrhoea extend from structural to chromosomal abnormalities. Polycystic ovarian syndrome (PCOS) is a common cause of secondary amenorrhoea but an uncommon cause of primary amenorrhoea. An early and prompt diagnosis of PCOS is important, as up to 30% of these women are predisposed to glucose intolerance and obesity, with the subgroup of women presenting with primary amenorrhoea and PCOS displaying a higher incidence of metabolic dysfunction. We describe a case of an 18-year-old female presenting with primary amenorrhoea of unknown aetiology. Although initial investigations did not demonstrate clinical or biochemical hyperandrogenism or any radiological evidence of polycystic ovaries, a raised luteinising hormone (LH) suggested a diagnosis of PCOS. If PCOS was the correct diagnosis, then one would expect intact hypothalamic GnRH and pituitary gonadotropin release. We used the novel hormone kisspeptin to confirm intact hypothalamic GnRH release and a GnRH stimulation test to confirm intact pituitary gonadotroph function. This case highlights that kisspeptin is a potential unique tool to test GnRH function in patients presenting with reproductive disorders. LEARNING POINTS: Polycystic ovarian syndrome (PCOS) can present with primary amenorrhoea, and therefore, should be considered in the differential diagnosis.PCOS is a heterogeneous condition that may present in lean women with few or absent signs of hyperandrogenism.GnRH stimulation tests are useful in evaluating pituitary function; however, to date, we do not have a viable test of GnRH function. Kisspeptin has the potential to form a novel diagnostic tool for assessing hypothalamic GnRH function by monitoring gonadotropin response as a surrogate marker of GnRH release.Confirmation of intact GnRH function helps consolidate a diagnosis in primary amenorrhoea and gives an indication of future fertility

Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative

Background: Echocardiography artificial intelligence (AI) requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardisation of such techniques. Methods: The training dataset were 2056individual frames drawn at random from 1265parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015-2016. Nine experts labelled these images using our online platform. From this, we trained a convolutional neural network to identify key points. Subsequently, 13 experts labelled a validation dataset of the end-systolic and end-diastolic frame from100new video-loops, twice each. The 26-opinionconsensus was used as the reference standard. The primary outcome was “precision SD”, the standard deviation of difference between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4mm. Intraclass correlation coefficient (ICC) between AI and expert consensus was 0.926 (95% CI 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8mm for AI (ICC 0.809; 0.729–0.967), versus 2.0 for individuals (ICC 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4mm for AI (ICC 0.535; 95% CI 0.379–0.661), versus 2.2mm for individuals(0.366; 0.288 to 0.462).We present all images and annotations. This highlights challenging cases, including poor image quality, tapered ventricles, and indistinct boundaries. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations and results openly