Magnetically induced anisotropic structure in an injectable hydrogel for skeletal muscle regeneration.

Skeletal muscle integrity and its intrinsic aligned architecture are crucial for locomotion, postural support, and respiration functions, impacting overall quality of life. However, volumetric muscle loss (VML) can exceed intrinsic regenerative potential, leading to fibrosis and impairments. Autologous muscle grafting, the current gold standard, is constrained by tissue availability and success rates. Therefore, innovative strategies like cell-based therapies and scaffold-based approaches are needed. Our minimally invasive approach involves a tunable injectable hydrogel capable of achieving an aligned architecture post-injection via a low-intensity static magnetic field (SMF). Our hydrogel formulation uses gellan gum as the backbone polymer, enriched with essential extracellular matrix components such as hyaluronic acid and collagen type I, enhancing bio-functionality. To achieve an aligned architectural biomimicry, collagen type I is coupled with iron oxide magnetic nanoparticles, creating magnetic collagen bundles (MagC) that align within the hydrogel when exposed to a SMF. An extensive study was performed to characterize MagC and assess the hydrogel's stability, mechanical properties, and biological response in vitro and in vivo. The proposed system, fully composed of natural polymers, exhibited mechanical properties similar to human skeletal muscle and demonstrated effective biological performances, supporting its potential as a safe and patient-friendly treatment for VML.

Relationship between endometriosis and uterine cervical elasticity assessed using ultrasound strain elastography.

PURPOSE: Internal cervical os (ICO) stiffness is related to menstrual pain, a key symptom of endometriosis. The study evaluated whether women with endometriosis have a stiffer ICO than unaffected women. METHODS: A retrospective cross-sectional analysis was conducted using prospectively collected data from women with and without endometriosis, spanning from June 2020 to September 2022. Endometriosis was diagnosed through clinical and ultrasound evaluations, with histological confirmation in a subset of participants. Strain elastography (SE) was employed to measure tissue elasticity in four cervical regions of interest: the ICO and the anterior, posterior, and middle cervical compartments (ACC, PCC, and MCC, respectively). Tissue elasticity was quantified using a color-based scoring system ranging from 0.1 (blue, indicating less elasticity) to 3.0 (red, indicating greater elasticity). RESULTS: Overall, 287 women were included, with 157 diagnosed with endometriosis and 130 controls. On SE, women with endometriosis exhibited a lower color score (mean±standard deviation), indicating lower elasticity, for the ICO (0.56±0.28 vs. 0.70±0.26, P=0.001) and PCC (0.69±0.30 vs. 0.80±0.27, P=0.002). Additionally, they had a lower ICO/MCC ratio (0.45±0.28 vs. 0.60±0.32, P=0.001) and ICO/ACC ratio (0.68±0.42 vs. 0.85±0.39, P=0.001). Multiple logistic regression analysis revealed that endometriosis was associated with the ICO color score (odds ratio, 0.053; 95% confidence interval, 0.014 to 0.202; R2=0.358; P=0.001), even after adjusting for confounding factors like the presence of myomas (P=0.040) and the use of hormonal therapy (P=0.001). The results were corroborated in women with histologically confirmed endometriosis (n=71). CONCLUSION: The findings suggest a potential relationship between a stiffer ICO and endometriosis.

Bio-based production of cis,cis-muconic acid as platform for a sustainable polymers production.

Production of the high industrial value cis,cis-muconic acid (ccMA) from renewable biomasses is of main interest especially when biological (green) processes are used. We recently generated a E. coli strain expressing five recombinant enzymes to convert vanillin (VA, from lignin) into ccMA. Here, we optimized a growing cell approach in bioreactor for the ccMA production. The medium composition, fermentation conditions, and VA addition were tuned: pulse-feeding VA at 1 mmol/h allowed to reach 5.2 g/L of ccMA in 48 h (0.86 g ccMA/g VA), with a productivity 4-fold higher compared to the resting cells approach, thus resulting in significantly lower E-factor and Process Mass Intensity green metric parameters. The recovered ccMA has been used as building block to produce a fully bioderived polymer with rubber-like properties. The sustainable optimized bioprocess can be considered an integrated approach to develop a platform for bio-based polymers production from renewable feedstocks.

Contactless magnetically responsive injectable hydrogel for aligned tissue regeneration.

Cellular alignment plays a pivotal role in several human tissues, including skeletal muscle, spinal cord and tendon. Various techniques have been developed to control cellular alignment using 3D biomaterials. However, the majority of 3D-aligned scaffolds require invasive surgery for implantation. In contrast, injectable hydrogels provide a non-invasive delivery method, gaining considerable attention for the treatment of diverse conditions, including osteochondral lesions, volumetric muscle loss, and traumatic brain injury. We engineered a biomimetic hydrogel with magnetic responsiveness by combining gellan gum, hyaluronic acid, collagen, and magnetic nanoparticles (MNPs). Collagen type I was paired with MNPs to form magnetic collagen bundles (MCollB), allowing the orientation control of these bundles within the hydrogel matrix through the application of a remote low-intensity magnetic field. This resulted in the creation of an anisotropic architecture. The hydrogel mechanical properties were comparable to those of human soft tissues, such as skeletal muscle, and proof of the aligned hydrogel concept was demonstrated. In vitro findings confirmed the absence of toxicity and pro-inflammatory effects. Notably, an increased fibroblast cell proliferation and pro-regenerative activation of macrophages were observed. The in-vivo study further validated the hydrogel biocompatibility and demonstrated the feasibility of injection with rapid in situ gelation. Consequently, this magnetically controlled injectable hydrogel exhibits significant promise as a minimally invasive, rapid gelling and effective treatment for regenerating various aligned human tissues.

Interpretation of Artificial Intelligence Models in Healthcare: A Pictorial Guide for Clinicians.

Artificial intelligence (AI) models can play a more effective role in managing patients with the explosion of digital health records available in the healthcare industry. Machine-learning (ML) and deep-learning (DL) techniques are two methods used to develop predictive models that serve to improve the clinical processes in the healthcare industry. These models are also implemented in medical imaging machines to empower them with an intelligent decision system to aid physicians in their decisions and increase the efficiency of their routine clinical practices. The physicians who are going to work with these machines need to have an insight into what happens in the background of the implemented models and how they work. More importantly, they need to be able to interpret their predictions, assess their performance, and compare them to find the one with the best performance and fewer errors. This review aims to provide an accessible overview of key evaluation metrics for physicians without AI expertise. In this review, we developed four real-world diagnostic AI models (two ML and two DL models) for breast cancer diagnosis using ultrasound images. Then, 23 of the most commonly used evaluation metrics were reviewed uncomplicatedly for physicians. Finally, all metrics were calculated and used practically to interpret and evaluate the outputs of the models. Accessible explanations and practical applications empower physicians to effectively interpret, evaluate, and optimize AI models to ensure safety and efficacy when integrated into clinical practice.

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.

Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks.

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

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.

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023).

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

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.

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.

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.

Oncological Outcomes of Thulium-Yttrium-Aluminum-Garnet (Tm:YAG) Laser Ablation for Penile Cancer.

OBJECTIVE: To report oncological outcomes after thulium-yttrium-aluminum-garnet (Tm:YAG) laser ablation for penile cancer patients. MATERIALS AND METHODS: We retrospectively analyzed 71 patients with ≤cT1 penile cancer (2013-2022). All patients underwent Tm:YAG ablation with a RevoLix 200W continuous-wave laser. First, Kaplan-Meier plots and multivariable Cox regression models tested local tumor recurrence rates. Second, Kaplan-Meier plots tested progression-free survival (≥T3 and/or N1-3 and/or M1). RESULTS: Median (interquartile range) follow-up time was 38 (22-58) months. Overall, 33 (50.5%) patients experienced local tumor recurrence. Specifically, 19 (29%) vs 9 (14%) vs 5 (7.5%) patients had 1 vs 2 vs 3 recurrences over time. In multivariable Cox regression models, a trend for higher recurrence rates was observed for G3 tumors (hazard ratio:6.1; P = .05), relative to G1. During follow-up, 12 (18.5%) vs 4 (6.0%) vs 2 (3.0%) men were retreated with 1 vs 2 vs 3 Tm:YAG laser ablations. Moreover, 11 (17.0%) and 3 (4.5%) patients underwent glansectomy and partial/total penile amputation. Last, 5 (7.5%) patients experienced disease progression. Specifically, TNM stage at the time of disease progression was: (1) pT3N0; (2) pT2N2; (3) pTxN3; (4) pT1N1 and (5) pT3N3, respectively. CONCLUSION: Tm:YAG laser ablation provides similar oncological results as those observed by other penile-sparing surgery procedures. In consequence, Tm:YAG laser ablation should be considered a valid alternative for treating selected penile cancer patients.

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.

Penile-Sparing Surgery for Tumour Recurrence after Previous Glansectomy/Partial Penectomy: Treatment Feasibility and Oncological Outcomes.

We tested the feasibility and oncological outcomes after penile-sparing surgery (PSS) for local recurrent penile cancer after a previous glansectomy/partial penectomy. We retrospectively analysed 13 patients (1997-2022) with local recurrence of penile cancer after a previous glansectomy or partial penectomy. All patients underwent PSS: circumcision, excision, or laser ablation. First, technical feasibility, treatment setting, and complications (Clavien-Dindo) were recorded. Second, Kaplan-Meier plots depicted overall and local recurrences over time. Overall, 11 (84.5%) vs. 2 (15.5%) patients were previously treated with glansectomy vs. partial penectomy. The median (IQR) time to disease recurrence was 56 (13-88) months. Six (46%) vs. two (15.5%) vs. five (38.5%) patients were treated with, respectively, local excision vs. local excision + circumcision vs. laser ablation. All procedures, except one, were performed in an outpatient setting. Only one Clavien-Dindo 2 complication was recorded. The median follow-up time was 41 months. Overall, three (23%) vs. four (30.5%) patients experienced local vs. overall recurrence, respectively. All local recurrences were safely treated with salvage surgery. In conclusion, we reported the results of a preliminary analysis testing safety, feasibility, and early oncological outcomes of PSS procedures for patients with local recurrence after previous glansectomy or partial penectomy. Stronger oncological outcomes should be tested in other series to optimise patient selection.

Quantitative analysis of prion disease using an AI-powered digital pathology framework.

Prion disease is a fatal neurodegenerative disorder characterized by accumulation of an abnormal prion protein (PrPSc) in the central nervous system. To identify PrPSc aggregates for diagnostic purposes, pathologists use immunohistochemical staining of prion protein antibodies on tissue samples. With digital pathology, artificial intelligence can now analyze stained slides. In this study, we developed an automated pipeline for the identification of PrPSc aggregates in tissue samples from the cerebellar and occipital cortex. To the best of our knowledge, this is the first framework to evaluate PrPSc deposition in digital images. We used two strategies: a deep learning segmentation approach using a vision transformer, and a machine learning classification approach with traditional classifiers. Our method was developed and tested on 64 whole slide images from 41 patients definitively diagnosed with prion disease. The results of our study demonstrated that our proposed framework can accurately classify WSIs from a blind test set. Moreover, it can quantify PrPSc distribution and localization throughout the brain. This could potentially be extended to evaluate protein expression in other neurodegenerative diseases like Alzheimer's and Parkinson's. Overall, our pipeline highlights the potential of AI-assisted pathology to provide valuable insights, leading to improved diagnostic accuracy and efficiency.

Reply to Taylor et al. Comment on "Manna et al. SARS-CoV-2 Inactivation in Aerosol by Means of Radiated Microwaves. Viruses 2023, 15, 1443".

SARS-CoV-2 is inactivated in aerosol (its primary mode of transmission) by means of radiated microwaves at frequencies that have been experimentally determined. Such frequencies are best predicted by the mathematical model suggested by Taylor, Margueritat and Saviot. The alignment between such mathematical prediction and the outcomes of our experiments serves to reinforce the efficacy of the radiated microwave technology and its promise in mitigating the transmission of SARS-CoV-2 in its naturally airborne state.

Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023).

Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.

EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population.

BACKGROUND: Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD: The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS: Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION: The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.