Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms.

BACKGROUND & AIMS: The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. METHODS: We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). RESULTS: Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. CONCLUSIONS: Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. IMPACT AND IMPLICATIONS: We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.

Reduction of Cardiac Fibrosis by Interference With YAP-Dependent Transactivation.

BACKGROUND: Conversion of cardiac stromal cells into myofibroblasts is typically associated with hypoxia conditions, metabolic insults, and/or inflammation, all of which are predisposing factors to cardiac fibrosis and heart failure. We hypothesized that this conversion could be also mediated by response of these cells to mechanical cues through activation of the Hippo transcriptional pathway. The objective of the present study was to assess the role of cellular/nuclear straining forces acting in myofibroblast differentiation of cardiac stromal cells under the control of YAP (yes-associated protein) transcription factor and to validate this finding using a pharmacological agent that interferes with the interactions of the YAP/TAZ (transcriptional coactivator with PDZ-binding motif) complex with their cognate transcription factors TEADs (TEA domain transcription factors), under high-strain and profibrotic stimulation. METHODS: We employed high content imaging, 2-dimensional/3-dimensional culture, atomic force microscopy mapping, and molecular methods to prove the role of cell/nuclear straining in YAP-dependent fibrotic programming in a mouse model of ischemia-dependent cardiac fibrosis and in human-derived primitive cardiac stromal cells. We also tested treatment of cells with Verteporfin, a drug known to prevent the association of the YAP/TAZ complex with their cognate transcription factors TEADs. RESULTS: Our experiments suggested that pharmacologically targeting the YAP-dependent pathway overrides the profibrotic activation of cardiac stromal cells by mechanical cues in vitro, and that this occurs even in the presence of profibrotic signaling mediated by TGF-ß1 (transforming growth factor beta-1). In vivo administration of Verteporfin in mice with permanent cardiac ischemia reduced significantly fibrosis and morphometric remodeling but did not improve cardiac performance. CONCLUSIONS: Our study indicates that preventing molecular translation of mechanical cues in cardiac stromal cells reduces the impact of cardiac maladaptive remodeling with a positive effect on fibrosis.

The Benefit of Transvaginal Elastography in Detecting Deep Endometriosis: A Feasibility Study.

OBJECTIVES: This study aimed to evaluate elastography features of deep infiltrating endometriosis (DIE), and to define whether this technique may discriminate lesions from surrounding non-endometriotic tissue. METHODS: This was an exploratory observational study on women affected by DIE treated in a third-level academic hospital gynaecology outpatient facility between 2020 and 2021. Strain elastography (SE) was conducted via transvaginal probe. Tissue deformation of DIE and surrounding tissue was expressed as percentage tissue deformation or as subjective colour score (CS; from blue=stiff to red=soft, assigned numerical values from 0 to 3). Ratios of normal tissue/DIE were compared to ratio of normal tissue/stiffer normal tissue area. RESULTS: Evaluations were performed on 46 DIE nodules and surrounding tissue of the uterosacral ligaments (n=21), parametrium (n=7), rectum (n=14), and recto-vaginal septum (n =4). Irrespective of location, DIE strain ratio (3.09, IQR 2.38-4.14 vs. 1.25, IQR 1.11-1.48; p<0.001) and CS ratio (4.62, IQR 3.83-6.94 vs. 1.13, IQR 1.06-1.29; p<0.001) was significantly higher than that of normal tissue. ROC AUC of CS ratio was higher than ROC AUC of strain ratio (99.76%, CI.95 99.26-100% vs. 91.35%, CI.95 85.23-97.47%; p=0.007), and best ROC threshold for CS ratio was 1.82, with a sensitivity of 97.83% (CI.95 93.48-100%) and a specificity of 100% (CI.95 100-100%). CONCLUSIONS: Both strain and CS ratios accurately distinguish DIE nodules at various locations. Applications of elastography in improving the diagnosis DIE, in distinguishing different DIE lesions and in monitoring DIE evolution can be envisioned and are worthy of further evaluation.

Controlled Liposome Delivery from Chitosan-Based Thermosensitive Hydrogel for Regenerative Medicine.

This work describes the development of an injectable nanocomposite system based on a chitosan thermosensitive hydrogel combined with liposomes for regenerative medicine applications. Liposomes with good physicochemical properties are prepared and embedded within the chitosan network. The resulting nanocomposite hydrogel is able to provide a controlled release of the content from liposomes, which are able to interact with cells and be internalized. The cellular uptake is enhanced by the presence of a chitosan coating, and cells incubated with liposomes embedded within thermosensitive hydrogels displayed a higher cell uptake compared to cells incubated with liposomes alone. Furthermore, the gelation temperature of the system resulted to be equal to 32.6 °C; thus, the system can be easily injected in the target site to form a hydrogel at physiological temperature. Given the peculiar performance of the selected systems, the resulting thermosensitive hydrogels are a versatile platform and display potential applications as controlled delivery systems of liposomes for tissue regeneration.

Thrombophilic alterations, migraine, and vascular disease: results from a case-control study.

BACKGROUND: The association between thrombophilic alterations, migraine, and vascular events has been broadly investigated but not been completely clarified. METHODS: In this cross-sectional, case-control study, we included consecutive outpatients diagnosed with migraine referring to a tertiary headache center. Migraine patients were matched to headache-free control subjects. All participants were evaluated for free protein S anticoagulant, functional protein C anticoagulant, homocysteine, and antiphospholipid antibodies (aPLs). History of ischemic stroke (IS) or transient ischemic attack (TIA), coronary heart disease, and peripheral venous thrombosis was also ascertained. RESULTS: We included 329 migraine patients and 329 control subjects (mean age 41 years, 77% women in both groups). Among migraine patients, 239 (72.6%) had migraine without aura and 90 (27.4%) had migraine with aura. Migraine patients had more frequently arterial hypertension, hypercholesterolemia, history of IS or TIA and, peripheral venous thrombosis compared to control subjects, whereas we found no differences in diabetes mellitus, BMI, and coronary heart disease between the two groups. At least one thrombophilic alteration was detected in 107 (32.5%) migraine patients and in 74 (22.5%) control subjects (OR = 1.66, 95% CI 1.17-2.35, p = 0.004). We identified an association of migraine with aPL positivity (OR = 2.6, 95% CI 1.5-4.7, p = 0.001) and with free protein S deficiency (OR = 4.7, 95% CI 1.6-14.0, p = 0.002), whereas we found no differences in protein C deficiency, APCR, and hyperhomocysteinemia between the two groups. Furthermore, aPL positivity and free protein S deficiency were more common in migraine patients with and without aura than in control subjects. We found that in migraine patients, aPL positivity was associated with both IS or TIA (OR = 5.6, 95% CI 1.5-20.4, p = 0.009) and with coronary heart disease (OR = 27.6, 95% CI 1.4-531.1, p = 0.028), whereas free protein S deficiency was associated with IS or TIA only (OR = 14.3, 95% CI 2.8-74.4, p = 0.002). CONCLUSIONS: Our research documented a significative higher prevalence of aPL positivity and protein S deficiency in migraineurs than in controls. Data also showed an association between these alterations and some vascular thrombotic events in migraine patients. We can argue that thrombophilic disorders associated with migraine may contribute to the occurrence of vascular events.

Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology.

PURPOSE: To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. METHODS: Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T1 and T2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. RESULTS: The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T1 and 4 ms for T2 ), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores ~2-4) were observed in clinically plausible regions (between lesions, corpus callosum). CONCLUSIONS: Brain T1 and T2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.

Coronavirus disease (COVID-19) in a paucisymptomatic patient: epidemiological and clinical challenge in settings with limited community transmission, Italy, February 2020.

Data concerning the transmission of the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) in paucisymptomatic patients are lacking. We report an Italian paucisymptomatic case of coronavirus disease 2019 with multiple biological samples positive for SARS-CoV-2. This case was detected using the World Health Organization protocol on cases and contact investigation. Current discharge criteria and the impact of extra-pulmonary SARS-CoV-2 samples are discussed.

Functional Outcome After Odontoid Fractures in the Elderly.

While several papers on mortality and the fusion rate in elderly patients treated surgically or non-surgically for odontoid fractures exist, little information is available on quality of life after treatment. The aim of treatment in these patients should not be fracture healing alone but also quality of life improvement.A literature search using PubMed identified seven papers including information on functional evaluation of 402 patients.Patients treated with anterior screw fixation had a good functional outcome in 92.6% of cases. This percentage seemed to decrease in octogenarians. Less information was available for patients treated with posterior approaches; it would seem that up to a half of such patients experienced pain and limitations in activities of daily living after surgery. Patients treated with a halo device had a functional outcome that was worse (or at least no better) than that of patients treated with surgery, with absence of limitations in activities of daily living in 77.3% of patients. Patients treated with a collar had a good functional outcome in the majority of cases, with absence of limitations in activities of daily living in 89% of patients.More studies are needed for evaluation of functional outcome, especially in patients treated with a collar, a halo device or a posterior approach.

Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.

BACKGROUND: Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues. RESULTS: The aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique. CONCLUSION: To the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.

Relationship between leukoaraiosis, carotid intima-media thickness and intima-media thickness variability: Preliminary results.

OBJECTIVE: To assess the relationship between the degree of leukoaraiosis (LA), carotid intima-media thickness (IMT) and intima-media thickness variability (IMTV). MATERIALS AND METHODS: Sixty-one consecutive patients, who underwent a brain MRI examination and a carotid artery ultrasound, were included in this retrospective study, which conformed with the Declaration of Helsinki. Written informed consent was waived. In each patient, right/left carotid arteries and brain hemispheres were assessed using automated software for IMT, IMTV and LA volume. RESULTS: The mean hemispheric LA volume was 2,224 mm3 (SD 2,702 mm3) and there was no statistically significant difference in LA volume between the right and left hemispheres (p value = 0.628). The mean IMT and IMTV values were 0.866 mm (SD 0.170) and 0.143 mm (SD 0.100), respectively, without significant differences between the right and left sides (p values 0.733 and 0.098, respectively). The correlation coefficient between IMTV and LA volume was 0.41 (p value = 0.0001), and 0.246 (p value = 0.074) between IMT and LA volume. CONCLUSIONS: IMTV significantly correlates with LA volume. Further studies are warranted to verify whether this parameter can be used clinically as a marker of cerebrovascular risk. KEY POINTS: • Intima-media thickness variability (IMTV) significantly correlates with leukoaraiosis volume. • IMTV could be used as a marker for cerebrovascular risk. • IMTV seems to be a better predictor of weighted mean difference than IMT.

Shape-based approach for coronary calcium lesion volume measurement on intravascular ultrasound imaging and its association with carotid intima-media thickness.

OBJECTIVES: Coronary calcification plays an important role in diagnostic classification of lesion subsets. According to histopathologic studies, vulnerable atherosclerotic plaque contains calcified deposits, and there can be considerable variation in the extent and degree of calcification. Intravascular ultrasound (IVUS) has demonstrated its role in imaging coronary arteries, thereby displaying calcium lesions. The aim of this work was to develop a fully automated system for detection, area and volume measurement, and characterization of the largest calcium deposits in coronary arteries. Furthermore, we demonstrate the correlation between the coronary calcium IVUS volume and the neurologic risk biomarker B-mode carotid intima-media thickness (IMT). METHODS: Our system automatically detects the frames with calcium, identifies the largest calcium region, and performs shape-based volume measurements. The carotid IMT is measured by using AtheroEdge software (AtheroPoint, LLC) on B-mode ultrasound imaging. RESULTS: Our database consists of low-contrast IVUS videos and corresponding B-mode images from 100 patients. Our experiments showed that the correlation between calcium volumes and carotid IMT was higher for the left carotid artery compared to the right carotid artery (r = 0.066 for the left carotid artery and 0.121 for the right carotid artery). We obtained 97% accuracy for automated calcium detection compared against the scoring given by our expert radiologists. Furthermore, we benchmarked shape-based volume measurement against the conventional method, which used integration of regions and showed a correlation of 84%. CONCLUSIONS: Since carotid IMT is an independent prognostic factor for myocardial infarction, and calcium lesions are correlated with stroke risk, we believe that this automated system for calcium volume measurement could be useful for assessing patients' cardiovascular risk.

An automated technique for carotid far wall classification using grayscale features and wall thickness variability.

PURPOSE: To test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images. METHODS: Our system (called Atheromatic) automatically computed the intima-media thickness (IMT) of the carotid far wall using AtheroEdge, calculated nonlinear features based on higher order spectra, and used these features and IMT and IMT variability (IMTVpoly ) to associate each image to a feature vector that was then labeled as symptomatic or asymptomatic (Sym/Asym) by a multiclassifiers system. We tested this method on a database of 118 carotid artery images from 37 symptomatic and 22 asymptomatic patients RESULTS: The highest accuracy (99.1%) was obtained by the support vector machine classifier using seven features. These features, relevant to discriminate Sym/Asym, included IMT and IMTVpoly , along with the bispectral entropies of the distal wall image at 77°, 78°, and 79° angles. CONCLUSIONS: Classification in Sym/Asym of the far carotid wall is feasible and accurate and could be useful for the early detection of atherosclerosis and to identify patients with higher cardiovascular risk.

Ankle-brachial index and its link to automated carotid ultrasound measurement of intima-media thickness variability in 500 Japanese coronary artery disease patients.

The purpose of this study was to evaluate whether the carotid intima-media thickness (cIMT) and intima-media thickness variability (IMTV) along the artery are correlated to the ankle-brachial index (ABI) in Japanese coronary artery disease patients. Five hundred consecutive patients (312 males; median age 69 ± 11 years) who underwent carotid ultrasonography and first coronary angiography were prospectively analyzed. By using automated software (AtheroEdge™, AtheroPoint, Roseville, CA, USA), we obtained the cIMT and IMTV. Pearson correlation analysis was performed to calculate the association between ABI, automatically measured cIMT, automatically measured IMTV, and the SYNTAX score. The mean cIMT was 0.881 ± 0.334 mm and the mean IMTV was 0.141 ± 0.112. IMTV was negatively and significantly correlated to ABI (ρ = -0.147; p = 0.001), whereas cIMT was not (ρ = -0.075; p = 0.097). IMTV and cIMT had the same significant correlation with the SYNTAX score. When we considered patients with a higher risk factor (ABI ≤ 0.9), we found higher values of IMTV and the SYNTAX score, but not higher values of cIMT. Logistic regression analysis showed that IMTV was independently associated with the complexity of the coronary artery disease (as assessed by the SYNTAX score). In conclusion, we show that IMTV automatically measured using AtheroEdge™ was correlated with ABI, whereas cIMT was not. IMTV could be integrated with cIMT measurement to improve the assessment of cardiovascular disease.

Semiautomated analysis of carotid artery wall thickness in MRI.

PURPOSE: To develop a semiautomatic method based on level set method (LSM) for carotid arterial wall thickness (CAWT) measurement. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) of diseased carotid arteries was acquired from 10 patients. Ground truth (GT) data for arterial wall segmentation was collected from three experienced vascular clinicians. The semiautomatic variational LSM was employed to segment lumen and arterial wall outer boundaries on 102 MR images. Two computer-based measurements, arterial wall thickness (WT) and arterial wall area (AWA), were computed and compared with GT. Linear regression, Bland-Altman, and bias correlation analysis on WT and AWA were applied for evaluating the performance of the semiautomatic method. RESULTS: Arterial wall thickness measured by radial distance measure (RDM) and polyline distance measure (PDM) correlated well between GT and variational LSM (r = 0.83 for RDM and r = 0.64 for PDM, P < 0.05). The absolute arterial wall area bias between LSM and three observers is less than 10%, suggesting LSM can segment arterial wall well compared with manual tracings. The Jaccard Similarity (Js ) analysis showed a good agreement for the segmentation results between proposed method and GT (Js 0.71 ± 0.08), the mean curve distance for lumen boundary is 0.34 ± 0.2 mm between the proposed method and GT, and 0.47 ± 0.2 mm for outer wall boundary. CONCLUSION: The proposed LSM can generate reasonably accurate lumen and outer wall boundaries compared to manual segmentation, and can work similar to a human reader. However, it tends to overestimate CAWT and AWA compared to the manual segmentation for arterial wall with small area.

Computer-aided diagnostic system for detection of Hashimoto thyroiditis on ultrasound images from a Polish population.

OBJECTIVES: Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis. METHODS: In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor. RESULTS: Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%. CONCLUSIONS: The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings.Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases.Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD: The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS: During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.

Automated anxiety detection using probabilistic binary pattern with ECG signals.

BACKGROUND AND AIM: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. MATERIALS AND METHODS: We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. RESULTS: Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. CONCLUSIONS: The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.