Single alpha-helices (SAHs) are increasingly recognized as important structural and functional elements of proteins. Comprehensive identification of SAH segments in large protein datasets was largely hindered by the slow speed of the most restrictive prediction tool for their identification, FT_CHARGE on common hardware. We have previously implemented an FPGA-based version of this tool allowing fast analysis of a large number of sequences. Using this implementation, we have set up of a semi-automated pipeline capable of analyzing full UniProt releases in reasonable time and compiling monthly updates of a comprehensive database of SAH segments. Releases of this database, denoted CSAHDB, is available on the CSAHserver 2 website at csahserver.itk.ppke.hu. An overview of human SAH-containing sequences combined with a literature survey suggests specific roles of SAH segments in proteins involved in RNA-based regulation processes as well as cytoskeletal proteins, a number of which is also linked to the development and function of synapses.

Background/Objectives: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. Methods: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients’ ^99mTc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images—75, 50, and 25% of total counts—which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. Results: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (p > 0.05). Conclusions: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time.

Purpose: Unilateral condylar hyperplasia (UCH) is a rare mandibular growth disorder in which accurate assessment of condylar metabolic activity is essential for surgical decision-making. Quantitative ^99m Tc-methylene diphosphonate (MDP) SPECT/CT is commonly used for this purpose; however, CT-based attenuation correction (CTAC) increases radiation exposure and may be affected by registration errors. Artificial intelligence (AI)-generated synthetic CT (SyCT) has been proposed as a CT-independent alternative. This study aimed to evaluate the agreement between SyCT-based attenuation correction (SyCTAC) and conventional CTAC in quantitative SPECT imaging of UCH. Methods: This retrospective study included 14 patients with UCH who underwent ^99m Tc-MDP SPECT/CT. SPECT images were reconstructed with identical parameters using either CTAC or AI-generated SyCTAC. Spherical volumes of interest were placed over both mandibular condyles and the clivus, and maximum and mean standardized uptake values were measured. Relative uptake fractions normalized to the summed condylar activity were calculated for the affected side, while condylar uptake normalized to the clivus was evaluated for both sides. Agreement between CTAC- and SyCTAC-derived indices was assessed using Bland–Altman analysis and linear mixed-effects models. Results: Visual evaluation revealed no relevant differences in image quality between the two reconstructions. Relative uptake fractions normalized to the summed condylar activity showed good agreement between methods. Clivus-normalized ratios demonstrated a small positive bias. Conclusion: AI-generated SyCT provides attenuation correction comparable to CTAC for clinically relevant relative condylar uptake assessment in UCH, supporting its use as a low-radiation alternative for quantitative mandibular SPECT imaging.

The solution of segmentation problems with deep neural networks requires a well-defined loss function for comparison and network training. In most network training approaches, only area-based differences that are of differing pixel matter are considered; the distribution is not. Our brain can compare complex objects with ease and considers both pixel level and topological differences simultaneously and comparison between objects requires a properly defined metric that determines similarity between them considering changes both in shape and values. In past years, topographic aspects were incorporated in loss functions where either boundary pixels or the ratio of the areas were employed in difference calculation. In this paper we will show how the application of a topographic metric, called wave loss, can be applied in neural network training and increase the accuracy of traditional segmentation algorithms. Our method has increased segmentation accuracy by 3% on both the Cityscapes and Ms-Coco datasets, using various network architectures.