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PubMedDate not supplied

Fundamental study of automatic cyto-screening for uterine cancer. I. Feature evaluation for the pattern recognition system.

A basic study was carried out to determine the parameters of a pattern recognition system for the automatic assessment of cytologic cell samples. Various cell features were extracted, whose combinations were evaluationed by an "ambiguity function". It was shown that the highest reliability can be obtained with a combination of the features of nuclear staining, nuclear area, area of cytoplasm, nuclear/cytoplasmic ratio, nuclear shape and chromatin pattern. However, recognition of the nuclear edge and chromatin patterns is complicated and makes automation difficult. Even if these two features are omitted, false positives do not exceed 20 per cent. Consequently screening of abnormal cells can be carried out by image recognition procedures by the use of a computer.

Cell Nucleus↗
PubMedDate not supplied

Fundamental study of automatic cyto-screening for uterine cancer. II. Segmentation of cells and computer simulation.

In images of Papanicolaou stained cells 64 gray levels have been differentiated by scanning densitomery. One of the two peaks in a differential histogram indicates the threshold of the cytoplasm, the other that of the nucleus. The two modes indicate where in the digitized image of good segmentation of the cell from its background and the nucleus from the cytoplasm can be accomplished.

Cell Nucleus↗
PubMed1977

[Continuous automatic analysis of the ECG with the aid of a computer with cardiosignal input directly from the patient. 1. Isolation of the active signal of the ECG, identification and measurement of its elements].

Because when fed to an on-line computer the ECG information carries a good deal of noise, of prime importance is devising a method for representation and filtration of the useful asignal. It is shown that in order to separate the ueful signal with a protracted direct ECG input from the patient to a computer the use of the proposed modified method of coherent accumulation is advisable. This modification envisages a step-wise solution of the following problems: current diagnosis of arrhythmic contractions exclusion of arrhythmic contractions from the accumulation procedure, automatic search of the normal duration of the RR intervals, equalization of the RRh intervals duration, statistical averaging of the obtained quantum values. The devised modification of the coherent accumulation method, while considerably increasing the noise-immunity of the algorhythm, enables it to effect a direct long-term automatic analysis of the ECG at intensive care units. The identification and measurement of the ECG waves and intervals are done through and automatic selection of the curve elements search zones, finding local maxima and also of the commencement and end of waves by using the method of the "moving window" with an adaptive evaluation of the first signal derivative intensity.

Adolescent↗
PubMed2026

Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation.

Evaluating hand and wrist radiographs is essential in pediatric endocrinology and clinical genetics, particularly for the assessment of suspected skeletal anomalies. In this study, we present Auto-Bone-Caliper, an automated system for the segmentation and length measurement of metacarpal and phalangeal (M&P) bones, trained and evaluated on public datasets comprising both normal and dysmorphic cases. We first introduce InstanceSAM, a two-stage framework that detects and segments all 19 M&P bones in pediatric hand radiographs, achieving Dice scores of 98.7% for normal bones and 95.0% for dysmorphic bones. We further develop and evaluate three methods for bone-length estimation, identifying a k-means-based approach as the most accurate, with relative errors of 2.2% for normal bones and 4.5% for dysmorphic bones. Our automated pipeline, Auto-Bone-Caliper, integrates InstanceSAM with the k-means-based length-estimation method. To enable scale-independent downstream analyses, we derive relative bone-length measures from the automated measurements. Using these relative measures, we statistically compare measurements obtained using Auto-Bone-Caliper on an independent dataset with a healthy reference catalog of normal bone morphologies, observing a high level of agreement (Wasserstein-1 distance = 0.012). Finally, we demonstrate a potential clinical use case of Auto-Bone-Caliper by obtaining relative metacarpophalangeal pattern profiles for three genetic conditions, namely Turner syndrome, achondroplasia, and pseudohypoparathyroidism. Our results highlight the potential of the Auto-Bone-Caliper to streamline and standardize M&P length measurement, providing an objective and reproducible tool suitable for clinical application.

Humans↗
PubMed2026

Hypernetwork-guided fusion with intra-class MixUp for breast cancer subtyping.

Accurate breast cancer subtyping guides treatment selection, yet histopathology captures morphology without molecular state, while genomic profiling captures molecular signatures without spatial context. Existing fusion methods rely on concatenation, or on attention applied only after each modality is encoded independently. This work identifies a scale-dependent asymmetry in the direction of cross-modal conditioning: the direction that performs best under limited samples is not the one that holds at scale, and the reversal is traced to the capacity of the modulation pathway rather than to the fusion principle. The comparison is carried out within a hypernetwork-guided framework in which an auxiliary network maps one modality to conditioning parameters that modulate the other's feature representation, shaping features at the parametric level rather than the decision stage; modulation is patient-specific rather than patch-specific. Both directions are instantiated-gene-to-image (HyperG2I) and image-to-gene (HyperI2G) - and trained under a label-aware MixUp strategy that interpolates within-class samples across both modalities, preserving the hard binary labels clinical decisions require. The framework is evaluated on two paired TCGA-BRCA cohorts-one limited-sample, one independently assembled at scale-under a single protocol spanning two whole-slide representations, multiple visual backbones, and both conditioning directions. On the limited-sample cohort, gene-to-image conditioning at its optimal augmentation setting exceeds early fusion and both unimodal baselines, giving the highest recall on the aggressive Basal/HER2 class of any configuration evaluated, and an ablation favours intra-class over inter-class mixing. At scale this ordering does not hold: image-to-gene conditioning sustains its performance whereas gene-to-image does not, recovering only partially under the full tissue bag and isolating the capacity of the modulation pathway as the binding constraint. Direction and capacity of cross-modal conditioning, rather than fusion depth alone, therefore govern how such frameworks scale.

Breast Neoplasms↗
PubMed2025

Cardiac CT fractal analysis of LV noncompaction and common cardiomyopathies.

BACKGROUND: Left ventricular noncompaction (LVNC), or hypertrabeculation, is a myocardial condition that remains challenging to diagnose and differentiate from other cardiomyopathies. This study evaluated the ability of cardiac CT to differentiate between LVNC, hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and controls using fractal analysis of LV trabeculae. METHODS: Subjects with LVNC, HCM, DCM, as well as controls, who underwent coronary CT angiography were included. LV trabecular structure was quantified using fractal analysis on a stack of 15 short-axis CT images. For each subject, maximum (FDmax) and average (FDglobal) fractal dimensions were reported. A subset of subjects also had clinically acquired cardiac MRI (CMR) exams for comparison. One-way ANOVA, Pearson correlation, and Bland-Altman analysis were used for statistical analysis. RESULTS: The study included 313 subjects (median age: 58.8 [48.1-68.0] years, 153 male) categorized into Control (89), LVNC (46), HCM (106), and DCM (72) cohorts. FDmax was significantly higher in LVNC (1.379 &#x200b;&#xb1; &#x200b;0.047) than in Control (1.305 &#x200b;&#xb1; &#x200b;0.033), HCM (1.321 &#x200b;&#xb1; &#x200b;0.040), and DCM (1.344 &#x200b;&#xb1; &#x200b;0.054) cohorts; all p &#x200b;< &#x200b;0.001. Similarly, FDglobal was significantly higher in LVNC (1.279 &#x200b;&#xb1; &#x200b;0.041) than in the other cohorts; all p &#x200b;< &#x200b;0.05. In a subset of 132 subjects with both CT and CMR exams, fractal dimensions from the two modalities were strongly correlated (r &#x200b;= &#x200b;0.63, p &#x200b;< &#x200b;0.0001), with CT-derived values being higher (1.337 &#x200b;&#xb1; &#x200b;0.049 vs. 1.262 &#x200b;&#xb1; &#x200b;0.045, p &#x200b;< &#x200b;0.0001). CONCLUSIONS: CT-derived fractal dimensions of LV trabecular structure were significantly higher in LVNC compared to control subjects, HCM, and DCM. CT-derived fractal dimensions strongly correlated with, but were higher than, those from cardiac MRI in the same subjects.

Humans↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed1976

Four-phase study of computer-assisted and slide-tape methods of stimulating clinical endodontic problems.

1. Computer-assisted instruction of stimulated clinical endodontic problems is superior to a slide-tape presentation for test selection but not for diagnosis and treatment planning. 2. The lack of a difference in diagnosis is likely due to the already superior performance of students in diagnosis at the University of Kentucky without computer assistance. A study with students of less background might reveal a difference in presentation methods. 3. Students with high GPSs score higher on a written test of endodontic clinical judgment. 4. Reliable results on the effects of a human tutor's supplementing machine instruction were not obtained. 5. Students felt that the problems presented in this study were useful in preparing for clinical treatment of patients. 6. After some exposure to machine methods of instruction, students divided into three sizable groups, one preferring a human teacher, another preferring a machine, and the third having no preference. The decision to use only machine or human instruction cannot then be made from student attitudes. 7. Students liked the active participation and immediate responses of the computer but not the time necessary to complete the problems. 8. Students liked the self-pacing, speed, and convenience of the slide-tape method but not the incompleteness of the problems presented by this method. 9. It appears that there is some justification from this study for offering both slide-tape and computer-assisted presentations to students.

Attitude↗
PubMed2026

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding-self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10&#x2009;&#xd7;&#x2009;Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.

Spatial Transcriptomics↗
PubMed2026

Changes in hippocampal functional connectivity and volume associated with cognitive improvement and decline in amnestic mild cognitive impairment following computerized cognitive training.

BACKGROUND: The hippocampus influences the outcomes of amnestic mild cognitive impairment (aMCI) and undergoes different changes during the cognitive decline or recovery of aMCI compared to elderly individuals with normal cognition, which may reveal disease-dependent neurodegeneration or plasticity. We first aimed to investigate the hippocampal changes associated with cognitive changes in aMCI using a combined case-control study design. METHODS: In total, 50&#x202f;aMCI individuals and 50 healthy controls (HCs) were recruited in Shenyang, China, and separately randomized into training and control groups: aMCI training group, aMCI no training group, HC training group, and HC no training group. The aMCI and HC training groups received computerized cognitive training (CCT) thrice weekly for 12 weeks. Cognitive assessments and MRI data were collected at baseline and follow-up. RESULTS: The primary outcome was significant CCT&#xd7;diagnosis interaction effect on the change in cognitive performance as measured by clock drawing test (CDT) scores (F&#x202f;=&#x202f;4.322, P&#x202f;=&#x202f;0.041); this interaction was driven by CCT specifically in aMCI (F&#x202f;=&#x202f;4.465, P&#x202f;=&#x202f;0.038). Significant CCT&#xd7;diagnosis interaction effects of right-hippocampal FC changes were observed in the bilateral precuneus/cuneus (Pvoxel<0.05) driven by CCT in aMCI (F&#x202f;=&#x202f;5.429, P&#x202f;=&#x202f;0.023), and in the left superior temporal gyrus/middle temporal gyrus (STG/MTG, Pvoxel<0.05), driven by CCT of only in HCs (F&#x202f;=&#x202f;6.587, P&#x202f;=&#x202f;0.013). A significant interaction effect of left-hippocampal FC changes were observed in the right triangular part of the inferior frontal gyrus (IFGtriang, Pvoxel<0.05), driven by CCT in aMCI and HCs (F&#x202f;=&#x202f;6.550, P&#x202f;=&#x202f;0.013; F&#x202f;=&#x202f;7.097, P&#x202f;=&#x202f;0.010). No significant interaction effect on the change in hippocampal GMV was noted (P&#x202f;>&#x202f;0.05). CONCLUSION: CCT can improve the visuospatial ability of aMCI, which is reflected by the CDT scores. CCT can alter hippocampal FC in the bilateral precuneus/cuneus, the right IFGtriang, and the left STG/MTG. The hippocampal GMV is difficult to change in both HCs and aMCI during the cognitive decline. REGISTRATION NUMBER: ChiCTR1900026849. DATE OF REGISTRATION: 24 October 2019 NAME OF TRIAL REGISTRY: Chinese Clinical Trial Registry (ChiCTR).

Humans↗