MV-MS-FETE: Multi-view multi-scale feature extractor and transformer encoder for stenosis recognition in echocardiograms

Background: aortic stenosis is a common heart valve disease that mainly affects older people in developed countries. Its early detection is crucial to prevent the irreversible disease progression and, eventually, death. A typical screening technique to detect stenosis uses echocardiograms; however, variations introduced by other tissues, camera movements, and uneven lighting can hamper the visual inspection, leading to misdiagnosis. To address these issues, effective solutions involve employing deep learning algorithms to assist clinicians in detecting and classifying stenosis by developing models that can predict this pathology from single heart views. Although promising, the visual information conveyed by a single image may not be sufficient for an accurate diagnosis, especially when using an automatic system; thus, this indicates that different solutions should be explored. Methodology: following this rationale, this paper proposes a novel deep learning architecture, composed of a multi-view, multi-scale feature extractor, and a transformer encoder (MV-MS-FETE) to predict stenosis from parasternal long and short-axis views. In particular, starting from the latter, the designed model extracts relevant features at multiple scales along its feature extractor component and takes advantage of a transformer encoder to perform the final classification. Results: experiments were performed on the recently released Tufts medical echocardiogram public dataset, which comprises 27,788 images split into training, validation, and test sets. Due to the recent release of this collection, tests were also conducted on several state-of-the-art models to create multi-view and single-view benchmarks. For all models, standard classification metrics were computed (e.g., precision, F1-score). The obtained results show that the proposed approach outperforms other multi-view methods in terms of accuracy and F1-score and has more stable performance throughout the training procedure. Furthermore, the experiments also highlight that multi-view methods generally perform better than their single-view counterparts. Conclusion: this paper introduces a novel multi-view and multi-scale model for aortic stenosis recognition, as well as three benchmarks to evaluate it, effectively providing multi-view and single-view comparisons that fully highlight the model's effectiveness in aiding clinicians in performing diagnoses while also producing several baselines for the aortic stenosis recognition task

Short communication: Characterization of molasses chemical composition

Beet and cane molasses are produced worldwide as a by-product of sugar extraction and are widely used in animal nutrition. Due to their composition, they are fed to ruminants as an energy source. However, molasses has not been properly characterized in the literature; its description has been limited to the type (sugarcane or beet) or to the amount of dry matter (DM), total or water-soluble sugars, crude protein, and ash. Our objective was to better characterize the composition of cane and beet molasses, examine possible differences, and obtain a proper definition of such feeds. For this purpose, 16 cane and 16 beet molasses samples were sourced worldwide and analyzed for chemical composition. The chemical analysis used in this trial characterized 97.4 and 98.3% of the compounds in the DM of cane and beet molasses, respectively. Cane molasses contained less DM compared with beet molasses (76.8 ± 1.02 vs. 78.3 ± 1.61%) as well as crude protein content (6.7 ± 1.8 vs. 13.5 ± 1.4% of DM), with a minimum value of 2.2% of DM in cane molasses and a maximum of 15.6% of DM in beet molasses. The amount of sucrose differed between beet and cane molasses (60.9 ± 4.4 vs. 48.8 ± 6.4% of DM), but variability was high even within cane molasses (39.2–67.3% of DM) and beet molasses. Glucose and fructose were detected in cane molasses (5.3 ± 2.7 and 8.1 ± 2.8% of DM, respectively), showing high variability. Organic acid composition differed as well. Lactic acid was more concentrated in cane molasses than in beet molasses (6.1 ± 2.8 vs. 4.5 ± 1.8% of DM), varying from 1.6 to 12.8% of DM in cane molasses. Dietary cation-anion difference showed numerical differences among cane and beet molasses (7 ± 53 vs. 66 ± 45 mEq/100 g of DM, on average). It varied from −76 to +155 mEq/100 g of DM in the cane group and from +0 to +162 mEq/100 g of DM in the beet group. Data obtained in this study detailed differences in composition between sources of molasses and suggested that a more complete characterization could improve the use of molasses in ration formulation