Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images

Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI

PURPOSE

To compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade.

MATERIALS AND METHODS

This retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models.

RESULTS

The area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model.

CONCLUSION

The bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Cardiac indices of driver fatigue across in-lab and on-road studies

Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states.

Triplet-constrained deep hashing for chest X-ray image retrieval in COVID-19 assessment

Radiology images of the chest, such as computer tomography scans and X-rays, have been prominently used in computer-aided COVID-19 analysis. Learning-based radiology image retrieval has attracted increasing attention recently, which generally involves image feature extraction and finding matches in extensive image databases based on query images. Many deep hashing methods have been developed for chest radiology image search due to the high efficiency of retrieval using hash codes. However, they often overlook the complex triple associations between images; that is, images belonging to the same category tend to share similar characteristics and vice versa. To this end, we develop a triplet-constrained deep hashing (TCDH) framework for chest radiology image retrieval to facilitate automated analysis of COVID-19. The TCDH consists of two phases, including (a) feature extraction and (b) image retrieval. For feature extraction, we have introduced a triplet constraint and an image reconstruction task to enhance discriminative ability of learned features, and these features are then converted into binary hash codes to capture semantic information. Specifically, the triplet constraint is designed to pull closer samples within the same category and push apart samples from different categories. Additionally, an auxiliary image reconstruction task is employed during feature extraction to help effectively capture anatomical structures of images. For image retrieval, we utilize learned hash codes to conduct searches for medical images. Extensive experiments on 30,386 chest X-ray images demonstrate the superiority of the proposed method over several state-of-the-art approaches in automated image search. The code is now available online.

Brachytherapy at the nanoscale with protein functionalized and intrinsically radiolabeled [169Yb]Yb2O3 nanoseeds

PURPOSE

Classical brachytherapy of solid malignant tumors is an invasive procedure which often results in an uneven dose distribution, while requiring surgical removal of sealed radioactive seed sources after a certain period of time. To circumvent these issues, we report the synthesis of intrinsically radiolabeled and gum Arabic glycoprotein functionalized [169Yb]Yb2O3 nanoseeds as a novel nanoscale brachytherapy agent, which could directly be administered via intratumoral injection for tumor therapy.

METHODS

169Yb (T½ = 32 days) was produced by neutron irradiation of enriched (15.2% in 168Yb) Yb2O3 target in a nuclear reactor, radiochemically converted to [169Yb]YbCl3 and used for nanoparticle (NP) synthesis. Intrinsically radiolabeled NP were synthesized by controlled hydrolysis of Yb3+ ions in gum Arabic glycoprotein medium. In vivo SPECT/CT imaging, autoradiography, and biodistribution studies were performed after intratumoral injection of radiolabeled NP in B16F10 tumor bearing C57BL/6 mice. Systematic tumor regression studies and histopathological analyses were performed to demonstrate therapeutic efficacy in the same mice model.

RESULTS

The nanoformulation was a clear solution having high colloidal and radiochemical stability. Uniform distribution and retention of the radiolabeled nanoformulation in the tumor mass were observed via SPECT/CT imaging and autoradiography studies. In a tumor regression study, tumor growth was significantly arrested with different doses of radiolabeled NP compared to the control and the best treatment effect was observed with ~ 27.8 MBq dose. In histopathological analysis, loss of mitotic cells was apparent in tumor tissue of treated groups, whereas no significant damage in kidney, lungs, and liver tissue morphology was observed.

CONCLUSIONS

These results hold promise for nanoscale brachytherapy to become a clinically practical treatment modality for unresectable solid cancers.

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.

Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion

BACKGROUND

Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns.

METHODS

We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021.

RESULTS

Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively.

CONCLUSIONS

Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.

The impact of patient scoliosis-specific exercises for adolescent idiopathic scoliosis: a systematic review and meta-analysis of randomized controlled trials with subgroup analysis using observational studies

PURPOSE

Adolescent idiopathic scoliosis (AIS) is a common pediatric spinal deformity frequently treated with patient scoliosis-specific exercises (PSSE). The purpose of this study is to perform a systematic review and meta-analysis of randomized controlled trials and sensitivity analysis of observational studies to determine the impact of PSSE on outcomes for AIS.

METHODS

A systematic review and meta-analysis on impact of PSSE for patients with AIS was performed. Databases used included PubMed, CINAHL, MEDLINE, Cochrane, and ScienceDirect database inception to October 2022. Inclusion criteria included use of PSSE, patient population of AIS, and full text.

RESULTS

A total of 26 articles out of 628 initial retrieved met final inclusion criteria (10 randomized controlled trials (RCTs), 16 observational studies). Total included patients (n = 2083) had a frequency weighted mean age of 13.2 ± 0.9 years and a frequency weighted mean follow-up of 14.5 ± 20.0 months. Based on only data from RCTs with direct comparison groups (n = 7 articles), there was a statistically significant but clinically insignificant improvement in Cobb angle of 2.5 degrees in the PSSE group (n = 152) as compared to the control group (n = 148; p = 0.017). There was no statistically significant improvement in Cobb angle when stratified by small curve (< 30 degrees) or large curve (> 30 degrees) with PSSE (p = 0.140 and p = 0.142, respectively). There was no statistically significant improvement in ATR (p = 0.326) or SRS-22 score (p = 0.370).

CONCLUSION

PSSE may not provide any clinically significant improvements in Cobb angle, ATR, or SRS-22 scores in patients with AIS. PSSE did not significantly improve Cobb angle when stratified by curve size.

LEVEL OF EVIDENCE

Level I.

Assessment of radio frequency fields in the 2.45 GHz band produced by smart home devices

This paper describes the assessment of the electromagnetic fields produced by consumer "smart" devices used to control and monitor everyday equipment and appliances in a modern "smart" home. The assessment is based on the careful measurement of fields produced by a range of such devices in a laboratory environment configured to operate in a condition simulating high user activity. All devices included in this study operate in the 2.4 GHz band utilizing either Wi-Fi or Bluetooth connectivity. Overall results indicate very low levels of electromagnetic fields for all IoT smart devices in terms of human exposure safety standards (typically much less than 1%) with very low duty cycles (also less than 1%) resulting in even lower time-averaged exposure levels. These low levels of exposure, along with rapid reduction of levels with distance from the devices, suggests that the cumulative effect of multiple devices in a "smart" home are not significant.

Dual-TranSpeckle: Dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling

The majority of existing deep learning-based image denoising algorithms mainly focus on processing the overall image features, ignoring the fine differences between the semantic and pixel features. Hence, we propose Dual-TranSpeckle (DTS), a medical ultrasound image despeckling network built on a dual-path Transformer. The DTS introduces two different paths, named "semantic path" and "pixel path," to facilitate the parallel transfer of feature information within the image. The semantic path passes a global view of the input semantic features, and the image features are passed through a Semantic Block to extract global semantic information from pixel-level features. The pixel path is employed to transmit finer-grained pixel features. Within the dual-path network framework, two essential modules, namely Dual Block and Merge Block, are designed. These leverage the Transformer architecture during the encoding and decoding stages. The Dual Block module facilitates information interaction between the semantic and pixel features by considering the interdependencies across both paths. Meanwhile, the Merge Block module enables parallel transfer of feature information by merging the dual path features, thereby facilitating the self-attention calculations for the overall feature representation. Our DTS is extensively evaluated on two public datasets and one private dataset. The DTS network demonstrates significant enhancements in quantitative evaluation results in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and naturalness image quality evaluator (NIQE). Furthermore, our qualitative analysis confirms that the DTS has significant improvements in despeckling performance, effectively suppressing speckle noise while preserving essential image structures.

Development of a CD163-Targeted PET Radiotracer That Images Resident Macrophages in Atherosclerosis

Tissue-resident macrophages are complementary to proinflammatory macrophages to promote the progression of atherosclerosis. The noninvasive detection of their presence and dynamic variation will be important to the understanding of their role in the pathogenesis of atherosclerosis. The goal of this study was to develop a targeted PET radiotracer for imaging CD163-positive (CD163+) macrophages in multiple mouse atherosclerosis models and assess the potential of CD163 as a biomarker for atherosclerosis in humans. Methods: CD163-binding peptide was identified using phage display and conjugated with a NODAGA chelator for 64Cu radiolabeling ([64Cu]Cu-ICT-01). CD163-overexpressing U87 cells were used to measure the binding affinity of [64Cu]Cu-ICT-01. Biodistribution studies were performed on wild-type C57BL/6 mice at multiple time points after tail vein injection. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages upregulated on the surface of atherosclerotic plaques were assessed in multiple mouse atherosclerosis models. Immunostaining, flow cytometry, and single-cell RNA sequencing were performed to characterize the expression of CD163 on tissue-resident macrophages. Human carotid atherosclerotic plaques were used to measure the expression of CD163+ resident macrophages and test the binding specificity of [64Cu]Cu-ICT-01. Results: [64Cu]Cu-ICT-01 showed high binding affinity to U87 cells. The biodistribution study showed rapid blood and renal clearance with low retention in all major organs at 1, 2, and 4 h after injection. In an ApoE-/- mouse model, [64Cu]Cu-ICT-01 demonstrated sensitive and specific detection of CD163+ macrophages and capability for tracking the progression of atherosclerotic lesions; these findings were further confirmed in Ldlr-/- and PCSK9 mouse models. Immunostaining showed elevated expression of CD163+ macrophages across the plaques. Flow cytometry and single-cell RNA sequencing confirmed the specific expression of CD163 on tissue-resident macrophages. Human tissue characterization demonstrated high expression of CD163+ macrophages on atherosclerotic lesions, and ex vivo autoradiography revealed specific binding of [64Cu]Cu-ICT-01 to human CD163. Conclusion: This work reported the development of a PET radiotracer binding CD163+ macrophages. The elevated expression of CD163+ resident macrophages on human plaques indicated the potential of CD163 as a biomarker for vulnerable plaques. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages warrant further investigation in translational settings.

Novel Technology Allowing Cone Beam Computed Tomography in 6 Seconds: A Patient Study of Comparative Image Quality

PURPOSE

The goal of this study was to evaluate the image quality provided by a novel cone beam computed tomography (CBCT) platform (HyperSight, Varian Medical Systems), a platform with enhanced reconstruction algorithms as well as rapid acquisition times. Image quality was compared with both status quo CBCT for image guidance, and to fan beam CT (FBCT) acquired on a CT simulator (CTsim).

METHODS AND MATERIALS

In a clinical study, 30 individuals were recruited for whom either deep inspiration (DIBH) or deep exhalation breath hold (DEBH) was used during imaging and radiation treatment of tumors involving liver, lung, breast, abdomen, chest wall, and pancreatic sites. All subjects were imaged during breath hold with CBCT on a standard image guidance platform (TrueBeam 2.7, Varian Medical Systems) and FBCT CT (CTsim, GE Optima). HyperSight imaging with both breath hold (HSBH) and free breathing (HSFB) was performed in a single session. The 4 image sets thus acquired were registered and compared using metrics quantifying artifact index, image nonuniformity, contrast, contrast-to-noise ratio, and difference of Hounsfield unit (HU) from CTsim.

RESULTS

HSBH provided less severe artifacts compared with both HSFB and TrueBeam. The severity of artifacts in HSBH images was similar to that in CTsim images, with statistically similar artifact index values. CTsim provided the best image uniformity; however, HSBH provided improved uniformity compared with both HSFB and TrueBeam. CTsim demonstrated elevated contrast compared with HyperSight imaging, but both HSBH and HSFB imaging showed superior contrast-to-noise ratio characteristics compared with TrueBeam. The median HU difference of HSBH from CTsim was within 1 HU for muscle/fat tissue, 12 HU for bone, and 14 HU for lung.

CONCLUSIONS

The HyperSight system provides 6-second CBCT acquisition with image artifacts that are significantly reduced compared with TrueBeam and comparable to those in CTsim FBCT imaging. HyperSight breath hold imaging was of higher quality compared with free breathing imaging on the same system. The median HU value in HyperSight breath hold imaging is within 15 HU of that in CTsim imaging for muscle, fat, bone, and lung tissue types, indicating the utility of image data for direct dose calculation in adaptive workflows.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images

BACKGROUND

Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.

PURPOSE

To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.

STUDY TYPE

Retrospective.

POPULATION

194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts.

FIELD STRENGTH/SEQUENCE

Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T.

ASSESSMENT

Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared.

STATISTICAL TESTS

DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant.

RESULTS

The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%).

DATA CONCLUSION

The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

Comparison of iterative reconstruction implementations for multislice helical CT

The most mature image reconstruction algorithms in multislice helical computed tomography are based on analytical and iterative methods. Over the past decades, several methods have been developed for iterative reconstructions that improve image quality by reducing noise and artifacts. In the regularization step of iterative reconstruction, noise can be significantly reduced, thereby making low-dose CT. The quality of the reconstructed image can be further improved by using model-based reconstructions. In these reconstructions, the main focus is on modeling the data acquisition process, including the behavior of the photon beams, the geometry of the system, etc. In this article, we propose two model-based reconstruction algorithms using a virtual detector for multislice helical CT. The aim of this study is to compare the effect of using a virtual detector on image quality for the two proposed algorithms with a model-based iterative reconstruction using the original detector model. Since the algorithms are implemented using multiple GPUs, the merging of separately reconstructed volumes can significantly affect image quality. This issue is often referred to as the "long object" problem, for which we also present a solution that plays an important role in the proposed reconstruction processes. The algorithms were evaluated using mathematical and physical phantoms, as well as patient cases. The SSIM, MS-SSIM and L1 metrics were utilized to evaluate the image quality of the mathematical phantom case. To demonstrate the effectiveness of the algorithms, we used the CatPhan 600 phantom. Additionally, anonymized patient scans were used to showcase the improvements in image quality on real scan data.

Developing a Computer Vision Model to Automate Quantitative Measurement of Hip-Knee-Ankle Angle in Total Hip and Knee Arthroplasty Patients

BACKGROUND

Increasing deformity of the lower extremities, as measured by the Hip-Knee-Ankle Angle (HKAA), is associated with poor patient outcomes after total hip and knee arthroplasty (THA, TKA). Automated calculation of HKAA is imperative to reduce the burden on orthopaedic surgeons. We proposed a detection-based deep learning (DL) model to calculate HKAA in THA and TKA patients and assessed the agreement between DL-derived HKAAs and manual measurement.

METHODS

We retrospectively identified 1,379 long-leg radiographs (LLR) from patients scheduled for THA or TKA within an academic medical center. There were 1,221 LLRs used to develop the model (randomly split into 70% training, 20% validation, and 10% held-out test sets); 158 LLRs were considered "difficult," as the femoral head was difficult to distinguish from surrounding tissue. There were two raters who annotated the HKAA of both lower extremities, and inter-rater reliability was calculated to compare the DL-derived HKAAs with manual measurement within the test set.

RESULTS

The DL model achieved a mean average precision of 0.985 on the test set. The average HKAA of the operative leg was 173.05 +/- 4.54°; the non-operative leg was 175.55 +/- 3.56°. The inter-rater reliability between manual and DL-derived HKAA measurements on the operative leg and non-operative leg indicated excellent reliability (Intraclass Correlation (ICC) (2,k) = 0.987 [0.96, 0.99], ICC (2,k) = 0.987 [0.98, 0.99, respectively]). The standard error of measurement for the DL-derived HKAA for the operative and non-operative legs was 0.515° and 0.403°, respectively.

CONCLUSION

A detection-based DL algorithm can calculate the HKAA in LLRs and is comparable to that calculated by manual measurement. The algorithm can detect the bilateral femoral head, knee, and ankle joints with high precision, even in patients where the femoral head is difficult to visualize.

Is the sustainability of exposure to non-ionizing electromagnetic radiation possible?

Technological advances imply an increase in artificially generating sources of electromagnetic fields (EMF), therefore, resulting in a permanent exposure of people and the environment (electromagnetic pollution). Inconsistent results have been published considering the evaluated health effects. The purpose of this study was to review scientific literature on EMF to provide a global and retrospective perspective, on the association between human exposure to non-ionizing radiation (NIR, mainly radiofrequency-EMF) and health and environmental effects. Studies on the health effects of 5G radiation exposure have not yet been performed with sufficient statistical power, as the exposure time is still relatively short and also the latency and intensity of exposure to 5G. The safety standards only consider thermal effects, do not contemplate non-thermal effects. We consider relevant to communicate this knowledge to the general public to improve education in this field, and to healthcare professionals to prevent diseases that may result from RF-EMF exposures.

A study of polarity effect for various ionization chambers in kilovoltage x-ray beams

BACKGROUND

Ionization chambers play an essential role in dosimetry measurements for kilovoltage (kV) x-ray beams. Despite their widespread use, there is limited data on the absolute values for the polarity correction factors across a range of commonly employed ionization chambers.

PURPOSE

This study aimed to investigate the polarity effects for five different ionization chambers in kV x-ray beams.

METHODS

Two plane-parallel chambers being the Advanced Markus and Roos and three cylindrical chambers; 3D PinPoint, Semiflex and Farmer chamber (PTW, Freiburg, Germany), were employed to measure the polarity correction factors. The kV x-ray beams were produced from an Xstrahl 300 unit (Xstrahl Ltd., UK). All measurements were acquired at 2 cm depth in a PTW-MP1 water tank for beams between 60 kVp (HVL 1.29 mm Al) and 300 kVp (HVL 3.08 mm Cu), and field sizes of 2-10 cm diameter for 30 cm focus-source distance (FSD) and 4 × 4 cm2 - 20 × 20 cm2 for 50 cm FSD. The ionization chambers were connected to a PTW-UNIDOS electrometer, and the polarity effect was determined using the AAPM TG-61 code of practice methodology.

RESULTS

The study revealed significant polarity effects in ionization chambers, especially in those with smaller volumes. For the plane-parallel chambers, the Advanced Markus chamber exhibited a maximum polarity effect of 2.5%, whereas the Roos chamber showed 0.3% at 150 KVp with the 10 cm circular diameter open-ended applicator. Among the cylindrical chambers at the same beam energy and applicator, the Pinpoint chamber exhibited a 3% polarity effect, followed by Semiflex with 1.7%, and Farmer with 0.4%. However, as the beam energy increased to 300 kVp, the polarity effect significantly increased reaching 8.5% for the Advanced Markus chamber and 13.5% for the PinPoint chamber at a 20 × 20 cm2 field size. Notably, the magnitude of the polarity effect increased with both the field size and beam energy, and was significantly influenced by the size of the chamber's sensitive volume.

CONCLUSIONS

The findings demonstrate that ionization chambers can exhibit substantial polarity effects in kV x-ray beams, particularly for those chambers with smaller volumes. Therefore, it is important to account for polarity corrections when conducting relative dose measurements in kV x-ray beams to enhance the dosimetry accuracy and improve patient dose calculations.

Study on Gamma sensory flicker for Insomnia

OBJECTIVES

Insomnia has been the subject of much systematic research because it is a risk factor for a variety of diseases. There is some evidence that gamma sensory stimulation therapy has also been demonstrated to improve sleep quality for people with Alzheimer's disease. However, it is unclear whether this method is effective for treating insomnia. The principal objective of this project was to investigate the efficacy and safety of gamma sensory flicker in improving the sleep quality of insomnia patients.

METHODS

Thirty-seven participants with insomnia were recruited for this prospective observational study. For a duration of 8 weeks, participants were exposed to flicker stimulation through a light and sound device.

RESULTS

During the main phase of the study, adherence rates averaged 92.21%. Additionally, no severe adverse events were reported for flicker treatment. Analysis of sleep diaries indicated that 40 Hz flickers can enhance sleep quality by reducing sleep onset latencies, and arousals, and increasing total sleep duration.

CONCLUSIONS

Gamma sensory flicker improves sleep quality in people suffering from insomnia.

Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction

PURPOSE

To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC.

METHODS

This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM).

RESULTS

T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively.

CONCLUSION

A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose significant challenges during the treatment process. Due to the importance of the issue, timely diagnosis of this illness can not only assist patients and their families in managing the condition but also enable early intervention, which may help prevent its advancement. EEG is a widely utilized technique for investigating mental disorders like SZ due to its non-invasive nature, affordability, and wide accessibility. In this study, our main goal is to develop an optimized system that can achieve automatic diagnosis of SZ with minimal input information. To optimize the system, we adopted a strategy of using single-channel EEG signals and integrated knowledge distillation and transfer learning techniques into the model. This approach was designed to improve the performance and efficiency of our proposed method for SZ diagnosis. Additionally, to leverage the pre-trained models effectively, we converted the EEG signals into images using Continuous Wavelet Transform (CWT). This transformation allowed us to harness the capabilities of pre-trained models in the image domain, enabling automatic SZ detection with enhanced efficiency. To achieve a more robust estimate of the model's performance, we employed fivefold cross-validation. The accuracy achieved from the 5-s records of the EEG signal, along with the combination of self-distillation and VGG16 for the P4 channel, is 97.81. This indicates a high level of accuracy in diagnosing SZ using the proposed method.

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.

Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review

BACKGROUND

Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction.

MAIN BODY

In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations.

SHORT CONCLUSION

As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis

Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.

Artificial intelligence and machine learning responses to COVID-19 related inquiries

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.

Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.

Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture

OBJECTIVES

Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus.

METHODS

A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases.

RESULTS

Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus.

CONCLUSION

Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.

Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer.Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n= 200), validation (n= 60), and testing sets (n= 62).Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively.Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.

Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier

Background

Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads.

Methods

This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm.

Results

The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate.

Conclusions

Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

Extrinsic Laryngeal Muscle Activity With Different Diameters and Water Depths in a Semi-Occluded Vocal Tract Exercise

PURPOSE

Surface electromyography (sEMG) has been used to evaluate extrinsic laryngeal muscle activity during swallowing and phonation. In the current study, sEMG amplitudes were measured from the infrahyoid and suprahyoid muscles during phonation through a tube submerged in water.

METHOD

The sEMG amplitude values measured from the extrinsic laryngeal muscles and the electroglottographic contact quotient (CQ) were obtained simultaneously from 62 healthy participants (31 men, 31 women) during phonation through a tube at six different depths (2, 4, 7, 10, 15, and 20 cm) while using two tubes with different diameters (1 and 0.5 cm).

RESULTS

With increasing depth, the sEMG amplitude for the suprahyoid muscles increased in men and women. However, sEMG amplitudes for the infrahyoid muscles increased significantly only in men. Tube diameter had a significant effect on the suprahyoid sEMG amplitudes only for men, with higher sEMG amplitudes when phonating with a 1.0-cm tube. CQ values increased with submerged depth for both men and women. Tube diameter affected results such than CQ values were higher for men when using the wider tube and for women with the narrower tube.

CONCLUSIONS

Vocal fold vibratory patterns changed with the depth of tube submersion in water for both men and women, but the patterns of muscle activation differed between the sexes. This suggests that men and women use different strategies when confronted with increased intraoral pressure during semi-occluded vocal tract exercises. In this study, sEMG provided insight into the mechanism for differences between vocally normal individuals and could help detect compensatory muscle activation during tube phonation in water for people with voice disorders.

Automatic measurement of lower limb alignment in portable devices based on deep learning for knee osteoarthritis

BACKGROUND

For knee osteoarthritis patients, analyzing alignment of lower limbs is essential for therapy, which is currently measured from standing long-leg radiographs of anteroposterior X-ray (LLR) manually. To address the time wasting, poor reproducibility and inconvenience of use caused by existing methods, we present an automated measurement model in portable devices for assessing knee alignment from LLRs.

METHOD

We created a model and trained it with 837 conforming LLRs, and tested it using 204 LLRs without duplicates in a portable device. Both manual and model measurements were conducted independently, then we recorded knee alignment parameters such as Hip knee ankle angle (HKA), Joint line convergence angle (JCLA), Anatomical mechanical angle (AMA), mechanical Lateral distal femoral angle (mLDFA), mechanical Medial proximal tibial angle (mMPTA), and the time required. We evaluated the model's performance compared with manual results in various metrics.

RESULT

In both the validation and test sets, the average mean radial errors were 2.778 and 2.447 (P<0.05). The test results for native knee joints showed that 92.22%, 79.38%, 87.94%, 79.82%, and 80.16% of the joints reached angle deviation<1° for HKA, JCLA, AMA, mLDFA, and mMPTA. Additionally, for joints with prostheses, 90.14%, 93.66%, 86.62%, 83.80%, and 85.92% of the joints reached that. The Chi-square test did not reveal any significant differences between the manual and model measurements in subgroups (P>0.05). Furthermore, the Bland-Altman 95% limits of agreement were less than ± 2° for HKA, JCLA, AMA, and mLDFA, and slightly more than ± 2 degrees for mMPTA.

CONCLUSION

The automatic measurement tool can assess the alignment of lower limbs in portable devices for knee osteoarthritis patients. The results are reliable, reproducible, and time-saving.

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Purpose

Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence.

Methods

In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively.

Results

The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate.

Conclusion

In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.

Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence

BACKGROUND

This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment parameters in pre- and post-osteotomy simulations.

METHODS

A total of 107 whole-leg standing radiographs were obtained from 107 patients who underwent HTO. First, the system detected anatomical landmarks on radiographs. Then, it simulated osteotomy and automatically measured five parameters in pre- and post-osteotomy simulation (hip knee angle [HKA], weight-bearing line ratio [WBL ratio], mechanical lateral distal femoral angle [mLDFA], mechanical medial proximal tibial angle [mMPTA], and mechanical lateral distal tibial angle [mLDTA]). The accuracy of the measured parameters was validated by comparing them with the ground truth (GT) values given by two orthopaedic surgeons.

RESULTS

All absolute errors of the system were within 1.5° or 1.5%. All inter-rater correlation confidence (ICC) values between the system and GT showed good reliability (>0.80). Excellent reliability was observed in the HKA (0.99) and WBL ratios (>0.99) for the pre-osteotomy simulation. The intra-rater difference of the system exhibited excellent reliability with an ICC value of 1.00 for all lower-limb alignment parameters in pre- and post-osteotomy simulations. In addition, the measurement time per radiograph (0.24 s) was considerably shorter than that of an orthopaedic surgeon (118 s).

CONCLUSION

The proposed system is practically applicable because it can measure lower-limb alignment parameters accurately and quickly in pre- and post-osteotomy simulations. The system has potential applications in surgical planning systems.

CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images

INTRODUCTION

Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images.

METHODS

The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images.

RESULTS

The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates.

CONCLUSION

The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.

Unveiling the hidden struggle of healthcare students as second victims through a systematic review

BACKGROUND

When healthcare students witness, engage in, or are involved in an adverse event, it often leads to a second victim experience, impacting their mental well-being and influencing their future professional practice. This study aimed to describe the efforts, methods, and outcomes of interventions to help students in healthcare disciplines cope with the emotional experience of being involved in or witnessing a mistake causing harm to a patient during their clerkships or training.

METHODS

This systematic review followed the PRISMA guidelines and includes the synthesis of eighteen studies, published in diverse languages from 2011 to 2023, identified from the databases MEDLINE, EMBASE, SCOPUS and APS PsycInfo. PICO method was used for constructing a research question and formulating eligibility criteria. The selection process was conducted through Rayyan. Titles and abstracts of were independently screened by two authors. The critical appraisal tools of the Joanna Briggs Institute was used to assess the risk of bias of the included studies.

RESULTS

A total of 1354 studies were retrieved, 18 met the eligibility criteria. Most studies were conducted in the USA. Various educational interventions along with learning how to prevent mistakes, and resilience training were described. In some cases, this experience contributed to the student personal growth. Psychological support in the aftermath of adverse events was scattered.

CONCLUSION

Ensuring healthcare students' resilience should be a fundamental part of their training. Interventions to train them to address the second victim phenomenon during their clerkships are scarce, scattered, and do not yield conclusive results on identifying what is most effective and what is not.

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study

BACKGROUND

We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions.

METHODS

Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson's correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics.

RESULTS

The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020).

CONCLUSIONS

3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC.

RELEVANCE STATEMENT

3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection.

KEY POINTS

• 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.

Continuous glucose monitoring for the routine care of type 2 diabetes mellitus

Although continuous glucose monitoring (CGM) devices are now considered the standard of care for people with type 1 diabetes mellitus, the uptake among people with type 2 diabetes mellitus (T2DM) has been slower and is focused on those receiving intensive insulin therapy. However, increasing evidence now supports the inclusion of CGM in the routine care of people with T2DM who are on basal insulin-only regimens or are managed with other medications. Expanding CGM to these groups could minimize hypoglycaemia while allowing efficient adaptation and escalation of therapies. Increasing evidence from randomized controlled trials and observational studies indicates that CGM is of clinical value in people with T2DM on non-intensive treatment regimens. If further studies confirm this finding, CGM could soon become a part of routine care for T2DM. In this Perspective we explore the potential benefits of widening the application of CGM in T2DM, along with the challenges that must be overcome for the evidence-based benefits of this technology to be delivered for all people with T2DM.

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images

BACKGROUND

COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies' diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid radiologists in detecting and quantifying COVID-19-related lung infections.

OBJECTIVE

To develop deep learning-based models to classify and quantify COVID-19-related lung infections.

METHODS

Initially, Dual Decoder Attention-based Semantic Segmentation Networks (DDA-SSNets) such as Dual Decoder Attention-UNet (DDA-UNet) and Dual Decoder Attention-SegNet (DDA-SegNet) are proposed to facilitate the dual segmentation tasks such as lung lobes and infection segmentation in chest X-ray (CXR) images. The lung lobe and infection segmentations are mapped to grade the severity of COVID-19 infection in both the lungs of CXRs. Later, a Genetic algorithm-based Deep Convolutional Neural Network classifier with the optimum number of layers, namely GADCNet, is proposed to classify the extracted regions of interest (ROI) from the CXR lung lobes into COVID-19 and non-COVID-19.

RESULTS

The DDA-SegNet shows better segmentation with an average BCSSDC of 99.53% and 99.97% for lung lobes and infection segmentations, respectively, compared with DDA-UNet with an average BCSSDC of 99.14% and 99.92%. The proposed DDA-SegNet with GADCNet classifier offered excellent classification results with an average BCCAC of 99.98%, followed by the GADCNet with DDA-UNet with an average BCCAC of 99.92% after extensive testing and analysis.

CONCLUSIONS

The results show that the proposed DDA-SegNet has superior performance in the segmentation of lung lobes and COVID-19-infected regions in CXRs, along with improved severity grading compared to the DDA-UNet and improved accuracy of the GADCNet classifier in classifying the CXRs into COVID-19, and non-COVID-19.

Radiographer training for screening of patients referred for Magnetic Resonance Imaging: A scoping review

INTRODUCTION

Strict safety practices are essential to ensure the safety of patients and staff in Magnetic Resonance Imaging (MRI). Training regarding the fundamentals of MRI safety is well-established and commonly agreed upon. However, more complex aspect of screening patients, such as image review or screening of unconscious patients/patients with communication difficulties is less well discussed. The current UK and USA guidelines do not suggest the use of communication training for MRI staff nor indicate any training to encourage reviewing images in the screening process. This review aims to map the current guidance regarding safety and patient screening training for MRI diagnostic and therapeutic radiographers.

METHODS

A systematic search of PubMed, Trip Medical database and Radiography journal was conducted. Studies were chosen based on the review objectives and pre-determined inclusion/exclusion criteria using the PRISMA-ScR framework.

RESULTS

Twenty-four studies were included in the review, which identified some key concepts including MRI safety training and delivery methods, screening and communication, screening of unconscious or non-ambulatory patients and the use of imaging.

CONCLUSION

Training gaps lie within the more complex elements of screening such as the inclusiveness of question phrasing, particularly to the neurodivergent population, how we teach radiographers to screen unconscious/unresponsive patients and using imaging to detect implants.

IMPLICATIONS FOR PRACTICE

The consequences of incomplete or inaccurate pre-MRI safety screening could be the introduction of unexpected implants into the scanner or forgoing MRI for a less desirable modality. The development of enhanced training programs in implant recognition using imaging and communication could complement existing training.

Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy

AIMS

Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer.

MATERIALS AND METHODS

Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison.

RESULTS

Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient.

CONCLUSION

Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the 'dose range' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.

2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation

Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy required for practical applications in current methods. To address this problem, we propose a 2.5D context-aware feature sequence fusion UNet (2.5D CFSF-UNet) model for esophageal tumor segmentation in CT sequence images. Specifically, we embed intra-slice multiscale attention feature fusion (Intra-slice MAFF) in each skip connection of UNet to improve feature learning capabilities, better expressing the differences between anatomical structures within CT sequence images. Additionally, the inter-slice context fusion block (Inter-slice CFB) is utilized in the center bridge of UNet to enhance the depiction of context features between CT slices, thereby preventing the loss of structural information between slices. Experiments are conducted on a dataset of 430 esophageal tumor patients. The results show an 87.13% dice similarity coefficient, a 79.71% intersection over union and a 2.4758 mm Hausdorff distance, which demonstrates that our approach can improve contouring consistency and can be applied to clinical applications.

Verification of neuronavigated TMS accuracy using structured-light 3D scans

Objective.To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined.Approach.The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated.Main results.The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location.Significance.TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.

Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.

Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS.

METHODS

We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.

RESULTS

In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.

CONCLUSIONS

Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Using the gamma-index analysis for inter-fractional comparison of in-beam PET images for head-and-neck treatment monitoring in proton therapy: A Monte Carlo simulation study

GOAL

In-beam Positron Emission Tomography (PET) is a technique for in-vivo non-invasive treatment monitoring for proton therapy. To detect anatomical changes in patients with PET, various analysis methods exist, but their clinical interpretation is problematic. The goal of this work is to investigate whether the gamma-index analysis, widely used for dose comparisons, is an appropriate tool for comparing in-beam PET distributions. Focusing on a head-and-neck patient, we investigate whether the gamma-index map and the passing rate are sensitive to progressive anatomical changes.

METHODS/MATERIALS

We simulated a treatment course of a proton therapy patient using FLUKA Monte Carlo simulations. Gradual emptying of the sinonasal cavity was modeled through a series of artificially modified CT scans. The in-beam PET activity distributions from three fields were evaluated, simulating a planar dual head geometry. We applied the 3D-gamma evaluation method to compare the PET images with a reference image without changes. Various tolerance criteria and parameters were tested, and results were compared to the CT-scans.

RESULTS

Based on 210 MC simulations we identified appropriate parameters for the gamma-index analysis. Tolerance values of 3 mm/3% and 2 mm/2% were suited for comparison of simulated in-beam PET distributions. The gamma passing rate decreased with increasing volume change for all fields.

CONCLUSION

The gamma-index analysis was found to be a useful tool for comparing simulated in-beam PET images, sensitive to sinonasal cavity emptying. Monitoring the gamma passing rate behavior over the treatment course is useful to detect anatomical changes occurring during the treatment course.

Dimensionality Engineering of Organic-Inorganic Halide Perovskites for Next-Generation X-Ray Detector

The next-generation X-ray detectors require novel semiconductors with low material/fabrication cost, excellent X-ray response characteristics, and robust operational stability. The family of organic-inorganic hybrid perovskites (OIHPs) materials comprises a range of crystal configuration (i.e., films, wafers, and single crystals) with tunable chemical composition, structures, and electronic properties, which can perfectly meet the multiple-stringent requirements of high-energy radiation detection, making them emerging as the cutting-edge candidate for next-generation X-ray detectors. From the perspective of molecular dimensionality, the physicochemical and optoelectronic characteristics of OIHPs exhibit dimensionality-dependent behavior, and thus the structural dimensionality is recognized as the key factor that determines the device performance of OIHPs-based X-ray detectors. Nevertheless, the correlation between dimensionality of OIHPs and performance of their X-ray detectors is still short of theoretical guidance, which become a bottleneck that impedes the development of efficient X-ray detectors. In the review, the advanced studies on the dimensionality engineering of OIHPs are critically assessed in X-ray detection application, discussing the current understanding on the "dimensionality-property" relationship of OIHPs and the state-of-the-art progresses on the dimensionality-engineered OIHPs-based X-ray detector, and highlight the open challenges and future outlook of this field.

Blood flow and emboli transport patterns during venoarterial extracorporeal membrane oxygenation: A computational fluid dynamics study

PROBLEM

Despite advances in Venoarterial Extracorporeal Membrane Oxygenation (VA-ECMO), a significant mortality rate persists due to complications. The non-physiological blood flow dynamics of VA-ECMO may lead to neurological complications and organ ischemia. Continuous retrograde high-flow oxygenated blood enters through a return cannula placed in the femoral artery which opposes the pulsatile deoxygenated blood ejected by the left ventricle (LV), which impacts upper body oxygenation and subsequent hyperoxemia. The complications underscore the critical need to comprehend the impact of VA-ECMO support level and return cannula size, as mortality remains a significant concern.

AIM

The aim of this study is to predict and provide insights into the complications associated with VA-ECMO using computational fluid dynamics (CFD) simulations. These complications will be assessed by characterising blood flow and emboli transport patterns through a comprehensive analysis of the influence of VA-ECMO support levels and arterial return cannula sizes.

METHODS

Patient-specific 3D aortic and major branch models, derived from a male patient's CT scan during VA-ECMO undergoing respiratory dysfunction, were analyzed using CFD. The investigation employed species transport and discrete particle tracking models to study ECMO blood (oxygenated) mixing with LV blood (deoxygenated) and to trace emboli transport patterns from potential sources (circuit, LV, and aorta wall). Two cannula sizes (15 Fr and 19 Fr) were tested alongside varying ECMO pump flow rates (50%, 70%, and 90% of the total cardiac output).

RESULTS

Cannula size did not significantly affect oxygen transport. At 90% VA-ECMO support, all arteries distal to the aortic arch achieved 100% oxygen saturation. As support level decreased, oxygen transport to the upper body also decreased to a minimum saturation of 73%. Emboli transport varied substantially between emboli origin and VAECMO support level, with the highest risk of cerebral emboli coming from the LV with a 15 Fr cannula at 90% support.

CONCLUSION

Arterial return cannula sizing minimally impacted blood oxygen distribution; however, it did influence the distribution of emboli released from the circuit and aortic wall. Notably, it was the support level alone that significantly affected the mixing zone of VA-ECMO and cardiac blood, subsequently influencing the risk of embolization of the cardiogenic source and oxygenation levels across various arterial branches.

Functional connectivity and complexity analyses of resting-state fMRI in pre-adolescents demonstrating the behavioral symptoms of ADHD

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), reward and motivation-related circuits (RMN), and salience network (SAL). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with the behavioral symptoms of ADHD, for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children demonstrating the behavioral features of ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in the relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents demonstrating the behavior symptoms of ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in the FPN and RMN in the healthy controls however the association was absent in the participants demonstrating the behavior symptoms of ADHD. The current findings of complexity and FC in ADHD pathology support hypotheses of altered function of inhibitory control networks in ADHD.