Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.

BackgroundArtificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs.HypothesisA neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs.AnimalsTraining dataset: 1398 frames from 461 canine echocardiograms from a single specialist center.Validation50 additional echocardiograms from the same center.MethodsTraining dataset: a right parasternal 4-chamber long axis frame from each study, labeled by 1 of 18 echocardiographers, marking anterior and posterior points of the septum and free wall.Validation datasetEnd-diastolic and end-systolic frames from 50 studies, annotated twice (blindly) by 13 experts, producing 26 measurements of each site from each frame. The neural network also made these measurements. We quantified its accuracy as the deviation from the expert consensus, using the individual-expert deviation from consensus as context for acceptable variation. The deviation of the AI measurement away from the expert consensus was assessed on each individual frame and compared with the root-mean-square-variation of the individual expert opinions away from that consensus.ResultsFor the septum in end-diastole, individual expert opinions deviated by 0.12 cm from the consensus, while the AI deviated by 0.11 cm (P = .61). For LVD, the corresponding values were 0.20 cm for experts and 0.13 cm for AI (P = .65); for the free wall, experts 0.20 cm, AI 0.13 cm (P Conclusions and clinical importanceAn artificial intelligence network can be trained to adequately measure linear LV dimensions, with performance indistinguishable from that of experts

Prevalence and Clinical Significance of Heart Murmurs Detected on Cardiac Auscultation in 856 Cats

Background: Cardiac auscultation is one of the most important clinical tools to identify patients with a potential heart disease. Although several publications have reported the prevalence of murmurs in cats, little information is available in relation to the exact origin of the blood flow turbulences responsible for these murmurs. The aim of this study was to determine the prevalence and clinical significance of murmurs detected during physical examination in cats. Methods: Retrospective evaluation of clinical records and echocardiographic examinations performed in cats for investigation of heart murmurs; Results: Records of 856 cats with full clinical information were available for review. The cause of murmur was identified in 93.1% of cases (72.3% with single blood flow turbulence, 26.4% with two, and 1.3% with three identifiable sources of murmur). Systolic anterior motion of the mitral valve (SAM) was the primary cause of murmur in this population (39.2%), followed by dynamic right ventricular outflow tract obstruction (DRVOTO) (32%) and flow murmurs (6.9%). Most cats with a murmur (56.7%) did not present any structural cardiac abnormality. Conclusions: This study indicates that some heart murmur characteristics (timing, loudness and point of maximal intensity) can potentially predict the presence of an underlying cardiac disease

Activation of the dopamine receptor type-2 (DRD2) promoter by 9-cis retinoic acid in a cellular model of Cushing's disease mediates the inhibition of cell proliferation and ACTH secretion without a complete corticotroph-to-melanotroph transdifferentiation

Cushing\u2019s disease (CD) is a rare condition in which hypercortisolemia is secondary to excessive ACTH release from a pituitary corticotroph adenoma. CD is associated with significant morbidity and mortality, and a safe therapy that effectively targets the pituitary tumor is still lacking. Retinoic acid (RA) and dopamine agonists (DAs) have recently been considered as monotherapy in CD patients, and satisfactory results have been reported, albeit in a limited number of patients. Given the permissive role of RA on the dopamine receptor type-2 (DRD2), the aim of present study was to see whether a combination of 9-cis RA and the DA bromocriptine (Br) might represent a possible treatment for CD. Here we show that 9-cis RA induces a functional DRD2 in the pituitary corticotroph cell line AtT20, and increases cell sensitivity to Br via a mechanism only partially related to corticotroph-to-melanotroph transdifferentiation. In addition, 9-cis RA and Br act synergistically to modulate cell viability, with favorable implications for clinical use. In nearly 45% of corticotropinoma-derived primary cultures, the combined administration of 9-cis RA and Br lowered the steady-state level of the ACTH precursor proopiomelanocortin (POMC) more efficiently than either of the drugs alone. In conclusion, the effects of a combination of 9-cis RA and Br on ACTH synthesis/secretion and cell viability in AtT20, and on POMC transcriptional activity in human corticotropinomas might represent a suitable starting point for assessing the potential of this treatment regimen for ACTH-secreting pituitary adenomas. This study thus has potentially important implications for novel therapeutic approaches to Cushing\u2019s disease

Aortic root/left ventricular diameters golden ratio in competitive athletes

Background: The athlete's heart is a well-known phenomenon characterized by a harmonic remodelling that affects the cardiac chambers. However, whether mild-to-moderate aortic dilatation can be considered normal in athletes is debated. This study aimed to evaluate the ratio between left ventricular (LV) size and aortic dimensions, reporting the normal values of the ratio between the aortic root diameters at the level of the sinuses of Valsalva and LV diameters (AoD/LVEDD ratio) in a wide cohort of competitive athletes. Materials and methods: Competitive athletes were compared with sedentary subjects and patients with aortic dilatation. 1901 subjects who underwent echocardiography from 2019 to 2022 were retrospectively enrolled: 993 athletes (74% males, mean age 26 ± 7 years), 410 sedentary (74.1% males, mean age 29 ± 11 years) and 498 patients with aortic dilatation (74.3% males, mean age 56 ± 7 years). Results: Patients with aortic dilatation had both an absolute (39.2 ± 2.4 mm) and indexed (19.4 ± 2.2 mm/m2) aortic diameter larger than athletes (30.6 ± 3.2 mm; 16.1 ± 1.5 mm/m2, p < 0.05) and sedentary subjects (30.5 ± 3.1 mm; 16.5 ± 1.6 mm/m2, p < 0.05), with no differences between athletes and sedentary subjects. The AoD/LVEDD ratio was lower in athletes (0.59 ± 0.06) compared to controls (0.65 ± 0.05, p < 0.05) and patients with aortic dilatation (0.81 ± 0.06, p < 0.05). The patients with aortopathy had the lowest LVEDD/AoD ratio, while competitive athletes had the highest, with values of 1.71 ± 0.16 in the latter (overall p value<0.001). Conclusions: In this study, we reported the AoD/LVEDD and LVEDD/AoD ratio values in a cohort of healthy athletes, additional parameters that could help confirm the harmonic remodelling in the athlete's heart

Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation

Abstract Background Artificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs. Hypothesis A neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs. Animals Training dataset: 1398 frames from 461 canine echocardiograms from a single specialist center. Validation: 50 additional echocardiograms from the same center. Methods Training dataset: a right parasternal 4‐chamber long axis frame from each study, labeled by 1 of 18 echocardiographers, marking anterior and posterior points of the septum and free wall. Validation Dataset End‐diastolic and end‐systolic frames from 50 studies, annotated twice (blindly) by 13 experts, producing 26 measurements of each site from each frame. The neural network also made these measurements. We quantified its accuracy as the deviation from the expert consensus, using the individual‐expert deviation from consensus as context for acceptable variation. The deviation of the AI measurement away from the expert consensus was assessed on each individual frame and compared with the root‐mean‐square‐variation of the individual expert opinions away from that consensus. Results For the septum in end‐diastole, individual expert opinions deviated by 0.12 cm from the consensus, while the AI deviated by 0.11 cm (P = .61). For LVD, the corresponding values were 0.20 cm for experts and 0.13 cm for AI (P = .65); for the free wall, experts 0.20 cm, AI 0.13 cm (P < .01). In end‐systole, there were no differences between individual expert and AI performances. Conclusions and Clinical Importance An artificial intelligence network can be trained to adequately measure linear LV dimensions, with performance indistinguishable from that of experts

Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.

Publication status: PublishedBACKGROUND: Artificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs. HYPOTHESIS: A neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs. ANIMALS: Training dataset: 1398 frames from 461 canine echocardiograms from a single specialist center. VALIDATION: 50 additional echocardiograms from the same center. METHODS: Training dataset: a right parasternal 4-chamber long axis frame from each study, labeled by 1 of 18 echocardiographers, marking anterior and posterior points of the septum and free wall. VALIDATION DATASET: End-diastolic and end-systolic frames from 50 studies, annotated twice (blindly) by 13 experts, producing 26 measurements of each site from each frame. The neural network also made these measurements. We quantified its accuracy as the deviation from the expert consensus, using the individual-expert deviation from consensus as context for acceptable variation. The deviation of the AI measurement away from the expert consensus was assessed on each individual frame and compared with the root-mean-square-variation of the individual expert opinions away from that consensus. RESULTS: For the septum in end-diastole, individual expert opinions deviated by 0.12 cm from the consensus, while the AI deviated by 0.11 cm (P = .61). For LVD, the corresponding values were 0.20 cm for experts and 0.13 cm for AI (P = .65); for the free wall, experts 0.20 cm, AI 0.13 cm (P < .01). In end-systole, there were no differences between individual expert and AI performances. CONCLUSIONS AND CLINICAL IMPORTANCE: An artificial intelligence network can be trained to adequately measure linear LV dimensions, with performance indistinguishable from that of experts

Evaluation of benazepril in cats with heart disease in a prospective, randomized, blinded, placebo‐controlled clinical trial

International audienceBackground:Heart disease is an important cause of morbidity and mortality in cats,but there is limited evidence of the benefit of any medication. Hypothesis:The angiotensin-converting enzyme inhibitor benazepril would delaythe time to treatment failure in cats with heart disease of various etiologies. Animals:One hundred fifty-one client-owned cats. Methods:Cats with heart disease, confirmed by echocardiography, with or withoutclinical signs of congestive heart failure, were recruited between 2002 and 2005and randomized to benazepril or placebo in a prospective, multicenter, parallel-group, blinded clinical trial. Benazepril (0.5-1.0 mg/kg) or placebo was administeredPO once daily for up to 2 years. The primary endpoint was treatment failure. Ana-lyses were conducted separately for all-cause treatment failure (main analysis) andheart disease-related treatment failure (supportive analysis). Results:No benefit of benazepril versus placebo was detected for time to all-causetreatment failure (P= .42) or time to treatment failure related to heart disease(P= .21). Hazard ratios (95% confidence interval [CI]) from multivariate analysis forbenazepril compared with placebo were 1.00 (0.57-1.74) for all-cause failure, and0.99 (0.50-1.94) for forward selection and 0.93 (0.48-1.81) for bidirectional selec-tion models for heart disease-related failure. There were no significant differencesbetween groups over time after administration of the test articles in left atriumdiameter, left ventricle wall thickness, quality of life scores, adverse events, orplasma biochemistry or hematology variables. Conclusions and Clinical Relevance:Benazepril was tolerated well in cats withheart disease, but no evidence of benefit was detecte