Proc Natl Acad Sci U S A. 2024 Nov 26;121(48):e2402028121. doi: 10.1073/pnas.2402028121. Epub 2024 Nov 18.

ABSTRACT

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980-2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

PMID:39556745 | DOI:10.1073/pnas.2402028121

PLoS One. 2024 Nov 18;19(11):e0312865. doi: 10.1371/journal.pone.0312865. eCollection 2024.

ABSTRACT

To understand and measure political information consumption in the high-choice media environment, we need new methods to trace individual interactions with online content and novel techniques to analyse and detect politics-related information. In this paper, we report the results of a comparative analysis of the performance of automated content analysis techniques for detecting political content in the German language across different platforms. Using three validation datasets, we compare the performance of three groups of detection techniques relying on dictionaries, classic supervised machine learning, and deep learning. We also examine the impact of different modes of data preprocessing on the low-cost implementations of these techniques using a large set (n = 66) of models. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by deep learning- and classic machine learning-based models, in contrast to the more robust performance of dictionary-based models on noisy data.

PMID:39556542 | DOI:10.1371/journal.pone.0312865

Neural Comput. 2024 Nov 18:1-34. doi: 10.1162/neco_a_01726. Online ahead of print.

ABSTRACT

In recent years, models based on the transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning. Numerous successful and efficient techniques, such as parameter-efficient fine-tuning and efficient scaling, have been proposed surrounding their applications to further enhance performance. However, the success of these strategies has always lacked the support of rigorous mathematical theory. To study the underlying mechanisms behind transformers and related techniques, we first propose a transformer learning framework motivated by distribution regression, with distributions being inputs, connect a two-stage sampling process with natural language processing, and present a mathematical formulation of the attention mechanism called attention operator. We demonstrate that by the attention operator, transformers can compress distributions into function representations without loss of information. Moreover, with the advantages of our novel attention operator, transformers exhibit a stronger capability to learn functionals with more complex structures than convolutional neural networks and fully connected networks. Finally, we obtain a generalization bound within the distribution regression framework. Throughout theoretical results, we further discuss some successful techniques emerging with large language models (LLMs), such as prompt tuning, parameter-efficient fine-tuning, and efficient scaling. We also provide theoretical insights behind these techniques within our novel analysis framework.

PMID:39556504 | DOI:10.1162/neco_a_01726

Skeletal Radiol. 2024 Nov 18. doi: 10.1007/s00256-024-04818-w. Online ahead of print.

ABSTRACT

OBJECTIVE: To determine an optimal region of interest (ROI) for opportunistic screening of osteoporosis in terms of short-term in vivo diagnostic precision.

MATERIALS AND METHODS: We included patients who underwent two CT scans and one dual-energy X-ray absorptiometry scan within a month in 2022. Deep-learning software automatically measured the attenuation in L1 using 54 ROIs (three slice thicknesses × six shapes × three intravertebral levels). To identify factors associated with a lower attenuation difference between the two CT scans, mixed-effect model analysis was performed with ROI-level (slice thickness, shape, intravertebral levels) and patient-level (age, sex, patient diameter, change in CT machine) factors. The root-mean-square standard deviation (RMSSD) and area under the receiver-operating-characteristic curve (AUROC) were calculated.

RESULTS: In total, 73 consecutive patients (mean age ± standard deviation, 69 ± 9 years, 38 women) were included. A lower attenuation difference was observed in ROIs in images with slice thicknesses of 1 and 3 mm than that in images with a slice thickness of 5 mm (p < .001), in large elliptical ROIs (p = .007 or < .001, respectively), and in mid- or cranial-level ROIs than that in caudal-level ROIs (p < .001). No patient-level factors were significantly associated with the attenuation difference. Large, elliptical ROIs placed at the mid-level of L1 on images with 1- or 3-mm slice thicknesses yielded RMSSDs of 12.4-12.5 HU and AUROCs of 0.90.

CONCLUSION: The largest possible regions of interest drawn in the mid-level trabecular portion of the L1 vertebra on thin-slice images may yield improvements in the precision of opportunistic screening for osteoporosis via CT.

PMID:39556270 | DOI:10.1007/s00256-024-04818-w

Endocrine. 2024 Nov 18. doi: 10.1007/s12020-024-04091-w. Online ahead of print.

ABSTRACT

BACKGROUND: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.

METHODS: Ultrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.

RESULTS: In the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.

CONCLUSIONS: This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.

PMID:39556263 | DOI:10.1007/s12020-024-04091-w

Pediatr Radiol. 2024 Nov 18. doi: 10.1007/s00247-024-06098-x. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric radiologists to essential AI concepts, including topics such as use case, data science, machine learning, deep learning, natural language processing, and generative AI as well as basics of AI training and validating. We outline the unique challenges of applying AI in pediatric imaging, such as data scarcity and distinct clinical characteristics, and discuss the current uses of AI in pediatric radiology, including both image interpretive and non-interpretive tasks. With this overview, we aim to equip pediatric radiologists with the foundational knowledge needed to engage with AI tools and inspire further exploration and innovation in the field.

PMID:39556194 | DOI:10.1007/s00247-024-06098-x

Eur Radiol Exp. 2024 Nov 18;8(1):129. doi: 10.1186/s41747-024-00527-0.

ABSTRACT

BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mammography.

METHODS: A retrospective dataset consisting of 152 examinations acquired with mammography machines from three different vendors was utilized. The blurred areas were contoured by expert breast radiologists. Normalized Wiener spectra (nWS) were extracted in a sliding window manner from each mammogram. These spectra served as input for a convolutional neural network (CNN) generating the probability of the spectra originating from a blurred region. The resulting blur probability mask, upon thresholding, facilitated the classification of a mammogram as either blurred or sharp. Ground truth for the test set was defined by the consensus of two radiologists.

RESULTS: A significant correlation between the view (p < 0.001), as well as between the laterality and the presence of blur (p = 0.004) was identified. The developed model AUROC of 0.808 (95% confidence interval 0.794-0.821) aligned with the consensus in 78% (67-83%) of mammograms classified as blurred. For mammograms classified by consensus as sharp, the model achieved agreement in 75% (67-83%) of them.

CONCLUSION: A model for blur detection was developed and assessed. The results indicate that a robust approach to blur detection, based on feature extraction in frequency space, tailored to radiologist expertise regarding clinical relevance, could eliminate the subjectivity associated with the visual assessment.

RELEVANCE STATEMENT: This blur detection model, if implemented in clinical practice, could provide instantaneous feedback to technicians, allowing for prompt mammogram retakes and ensuring that only high-quality mammograms are sent for screening and diagnostic tasks.

KEY POINTS: Blurring in mammography limits radiologist interpretation and diagnostic accuracy. This objective blur detection tool ensures image quality, and reduces retakes and unnecessary exposures. Wiener spectrum analysis and CNN enabled automated blur detection in mammography.

PMID:39556167 | DOI:10.1186/s41747-024-00527-0

Int J Legal Med. 2024 Nov 18. doi: 10.1007/s00414-024-03370-5. Online ahead of print.

ABSTRACT

This article evaluates the criteria for diatom testing in forensic investigations, focusing on drowning cases. Diatoms, unicellular algae found in aquatic environments, are critical to the determination of drowning because water containing diatoms is inhaled during submersion. The primary objectives include defining the exact amount and type of tissue to be analyzed, expressed in terms of diatom concentration relative to tissue weight, and detailing the conditions under which water samples are collected to study the diatom flora at the site. In addition, the importance of accurately identifying diatom taxa and comparing them by unit weight is emphasized. To improve the reliability of diatom testing, the study discusses advanced methods such as microwave digestion, vacuum filtration, and automated scanning electron microscopy (MD-VF-Auto SEM), which offer higher sensitivity and specificity. The integration of DNA sequencing and deep learning techniques is explored, offering promising improvements in diatom detection and classification. These advances aim to reduce false positives and improve the accuracy of determining drowning as the cause of death. The article highlights the need for standardized protocols for diatom testing to ensure consistency and reliability. By incorporating new technologies and refining existing methods, the forensic application of diatom testing can be significantly improved, allowing for more accurate and reliable conclusions in drowning investigations.

PMID:39556128 | DOI:10.1007/s00414-024-03370-5

J Hand Surg Am. 2024 Nov 15:S0363-5023(24)00432-5. doi: 10.1016/j.jhsa.2024.09.008. Online ahead of print.

ABSTRACT

PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing low bone mineral density require specialized dual x-ray absorptiometry (DXA) scans. Plain hand radiographs may have utility as an alternative screening tool, although optimal diagnostic radiographic parameters are unknown, and measurement is prone to human error. The aim of the present study was to develop and validate an artificial intelligence algorithm to screen for osteoporosis and osteopenia using standard hand radiographs.

METHODS: A cohort of patients with both a DXA scan and a plain hand radiograph within 12 months of one another was identified. Hand radiographs were labeled as normal, osteopenia, or osteoporosis based on corresponding DXA hip T-scores. A deep learning algorithm was developed using the ResNet-50 framework and trained to predict the presence of osteoporosis or osteopenia on hand radiographs using labeled images. The results from the algorithm were validated using a separate balanced validation set, with the calculation of sensitivity, specificity, accuracy, and receiver operating characteristic curve using definitions from corresponding DXA scans as the reference standard.

RESULTS: There was a total of 687 images in the normal category, 607 images in the osteopenia category, and 130 images in the osteoporosis category for a total of 1,424 images. When predicting low bone density (osteopenia or osteoporosis) versus normal bone density, sensitivity was 88.5%, specificity was 65.4%, overall accuracy was 80.8%, and the area under the curve was 0.891, at the standard threshold of 0.5. If optimizing for both sensitivity and specificity, at a threshold of 0.655, the model achieved a sensitivity of 84.6% at a specificity of 84.6%.

CONCLUSIONS: The findings represent a possible step toward more accessible, cost-effective, automated diagnosis and therefore earlier treatment of osteoporosis/osteopenia.

TYPE OF STUDY/LEVEL OF EVIDENCE: Diagnostic II.

PMID:39556066 | DOI:10.1016/j.jhsa.2024.09.008

J Exp Biol. 2024 Nov 18:jeb.249232. doi: 10.1242/jeb.249232. Online ahead of print.

ABSTRACT

The accelerometer, an onboard sensor, enables remote monitoring of animal posture and movement, allowing researchers to deduce behaviors. Despite the automated analysis capabilities provided by deep learning, data scarcity remains a challenge in ecology. We explored transfer learning to classify behaviors from acceleration data of critically endangered hawksbill sea turtles (Eretmochelys imbricata). Transfer learning reuses a model trained on one task from a large dataset to solve a related task. We applied this method using a model trained on green turtles (Chelonia mydas) and adapted it to identify hawksbill behaviors like swimming, resting, and feeding. We also compared this to a model trained on human activity data. Results showed an 8% and 4% F1-score improvement with transfer learning from green turtle and human datasets, respectively. Transfer learning allows researchers to adapt existing models to their study species, leveraging deep learning and expanding the use of accelerometers for wildlife monitoring.

PMID:39555892 | DOI:10.1242/jeb.249232

Cancer Sci. 2024 Nov 18. doi: 10.1111/cas.16374. Online ahead of print.

ABSTRACT

Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical diagnostic and typing tests are complex and time-consuming. In particular, all of these tests rely on bone marrow aspiration, which is invasive and leads to poor patient compliance, exacerbating treatment delays. Morphological analysis of peripheral blood cells (PBC) is still primarily used to distinguish between benign and malignant hematologic disorders, but it remains a challenge to diagnose and type these diseases solely by direct observation of peripheral blood(PB) smears by human experts. In this study, we apply a segmentation-based enhanced residual network that uses progressive multigranularity training with jigsaw patches. It is trained on a self-built annotated dataset of 21,208 images from 237 patients, including five types of benign white blood cells(WBCs) and eight types of leukemic cells. The network is not only able to discriminate between benign and malignant cells, but also to typify leukemia using a single peripheral blood cell. The network effectively differentiated acute promyelocytic leukemia (APL) from other types of acute myeloid leukemia (non-APL), achieving a precision rate of 89.34%, a recall rate of 97.37%, and an F1 score of 93.18% for APL. In contrast, for non-APL cases, the model achieved a precision rate of 92.86%, but a recall rate of 74.63% and an F1 score of 82.75%. In addition, the model discriminates acute lymphoblastic leukemia(ALL) with the Ph chromosome from those without. This approach could improve patient compliance and enable faster and more accurate typing of leukemias for early diagnosis and treatment to improve survival.

PMID:39555724 | DOI:10.1111/cas.16374

Br J Cardiol. 2024 Apr 16;31(2):015. doi: 10.5837/bjc.2024.015. eCollection 2024.

ABSTRACT

Artificial intelligence (AI) will revolutionise cardiology practices over the next decade, from optimising diagnostics to individualising treatment strategies. Moreover, it can play an important role in combating gender inequalities in cardiovascular disease outcomes. There is growing evidence that AI algorithms can match humans at echocardiography analysis, while also being able to extract subtle differences that the human eye cannot detect. Similar promise is evident in the analysis of electrocardiograms, creating a new layer of interpretation. From big data, AI can produce algorithms that individualise cardiac risk factors and prevent perpetuating gender biases in diagnosis. Nonetheless, AI implementation requires caution. To avoid worsening health inequalities, it must be trained across diverse populations, and when errors arise, a robust regulatory framework must be in place to ensure safety and accountability. AI is perfectly positioned to capitalise on the growth of big data, but to proceed we require a generation of physicians who understand its fundamentals.

PMID:39555461 | PMC:PMC11562571 | DOI:10.5837/bjc.2024.015

Indian J Urol. 2024 Oct-Dec;40(4):221-228. doi: 10.4103/iju.iju_39_24. Epub 2024 Oct 1.

ABSTRACT

INTRODUCTION: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions.

METHODS: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case-control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: "artificial intelligence," "cystoscopy," and "bladder cancer." The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses-Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve.

RESULTS: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908-0.976) and 0.957 (95% CI: 0.923-0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42-887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982-0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones.

CONCLUSIONS: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors.

PMID:39555437 | PMC:PMC11567573 | DOI:10.4103/iju.iju_39_24

J Thorac Dis. 2024 Oct 31;16(10):7096-7110. doi: 10.21037/jtd-24-244. Epub 2024 Oct 30.

ABSTRACT

Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare. This dynamic landscape has embraced the incorporation of advanced machine learning and deep learning techniques, particularly artificial intelligence (AI), into the realm of precision medicine. These groundbreaking innovations create fertile ground for the development of AI-based models adept at extracting valuable insights to inform clinical decisions, with the potential to quantitatively interpret patient data and impact overall patient outcomes significantly. In this comprehensive narrative review, a synthesis of various studies is presented, with a specific focus on three core areas aimed at providing clinicians with a practical understanding of AI-based technologies' potential applications in the diagnosis and management of non-small cell lung cancer (NSCLC). The emphasis is placed on methods for diagnosing malignancy in lung lesions, approaches to predicting histology and other pathological characteristics, and methods for predicting NSCLC gene mutations. The review culminates in a discussion of current trends and future perspectives within the domain of AI-based models, all directed toward enhancing patient care and outcomes in NSCLC. Furthermore, the review underscores the synthesis of diverse studies, accentuating AI applications in NSCLC diagnosis and management. It concludes with a forward-looking discussion on current trends and future perspectives, highlighting the LANTERN Study as a pioneering force set to elevate patient care and outcomes to unprecedented levels.

PMID:39552872 | PMC:PMC11565297 | DOI:10.21037/jtd-24-244

Curr Cardiovasc Risk Rep. 2024;18(12):187-195. doi: 10.1007/s12170-024-00752-7. Epub 2024 Nov 4.

ABSTRACT

PURPOSE OF REVIEW: Peripheral Artery Disease (PAD), a condition affecting millions of patients, is often underdiagnosed due to a lack of symptoms in the early stages and management can be complex given differences in genetic and phenotypic characteristics. This review aims to provide readers with an update on the utility of machine learning (ML) in the management of PAD.

RECENT FINDINGS: Recent research leveraging electronic health record (EHR) data and ML algorithms have demonstrated significant advances in the potential use of automated systems, namely artificial intelligence (AI), to accurately identify patients who might benefit from further PAD screening. Additionally, deep learning algorithms can be used on imaging data to assist in PAD diagnosis and automate clinical risk stratification.ML models can predict major adverse cardiovascular events (MACE) and major adverse limb events (MALE) with considerable accuracy, with many studies also demonstrating the ability to more accurately risk stratify patients for deleterious outcomes after surgical intervention. These predictions can assist physicians in developing more patient-centric treatment plans and allow for earlier, more aggressive management of modifiable risk-factors in high-risk patients. The use of proteomic biomarkers in ML models offers a valuable addition to traditional screening and stratification paradigms, though clinical utility may be limited by cost and accessibility.

SUMMARY: The application of AI to the care of PAD patients may enable earlier diagnosis and more accurate risk stratification, leveraging readily available EHR and imaging data, and there is a burgeoning interest in incorporating biological data for further refinement. Thus, the promise of precision PAD care grows closer. Future research should focus on validating these models via real-world integration into clinical practice and prospective evaluation of the impact of this new care paradigm.

PMID:39552745 | PMC:PMC11567977 | DOI:10.1007/s12170-024-00752-7

Pediatr Dev Pathol. 2024 Nov 18:10935266241299073. doi: 10.1177/10935266241299073. Online ahead of print.

ABSTRACT

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinical potential. Digital pathology is now a proven technology, enabling generation of high-resolution digital images from glass slides (whole slide images; WSI). WSIs facilitates AI-based image analysis to aid pathologists in diagnostic tasks, improve workflow efficiency, and address workforce shortages. Example applications include tumor segmentation, disease classification, detection, quantitation and grading, rare object identification, and outcome prediction. While advancements have occurred, integration of WSI-AI into clinical laboratories faces challenges, including concerns regarding evidence quality, regulatory adaptations, clinical evaluation, and safety considerations. In pediatric and developmental histopathology, adoption of AI could improve diagnostic efficiency, automate routine tasks, and address specific diagnostic challenges unique to the specialty, such as standardizing placental pathology and developmental autopsy findings, as well as mitigating staffing shortages in the subspeciality. Additionally, AI-based tools have potential to mitigate medicolegal implications by enhancing reproducibility and objectivity in diagnostic evaluations. An overview of recent developments and challenges in applying AI to pediatric and developmental pathology, focusing on machine learning methods applied to WSIs of pediatric pathology specimens is presented.

PMID:39552500 | DOI:10.1177/10935266241299073

Acta Radiol. 2024 Nov 18:2841851241292528. doi: 10.1177/02841851241292528. Online ahead of print.

ABSTRACT

BACKGROUND: Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).

PURPOSE: To investigate the feasibility of utilizing multi-parametric MRI-based deep transfer learning radiomics (DTLR) for the classification of lung adenocarcinoma (LUAD) and non-LUAD BMs.

MATERIAL AND METHODS: A retrospective analysis was performed on 342 patients with 1389 BMs. These instances were randomly assigned to a training set of 273 (1179 BMs) and a testing set of 69 (210 BMs) in an 8:2 ratio. Eight machine learning algorithms were employed to construct the radiomics models. A DL model was developed using four pre-trained convolutional neural networks (CNNs). The DTLR model was formulated by integrating the optimal performing radiomics model and the DL model using a classification probability averaging approach. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the models.

RESULTS: The AUC for the optimal radiomics and DL model in the testing set were 0.824 (95% confidence interval [CI]= 0.726-0.923) and 0.775 (95% CI=0.666-0.884), respectively. The DTLR model demonstrated superior discriminatory power, achieving an AUC of 0.880 (95% CI=0.803-0.957). In addition, the DTLR model exhibited good consistency between actual and predicted probabilities based on the calibration curve and DCA analysis, indicating its significant clinical value.

CONCLUSION: Our study's DTLR model demonstrated high diagnostic accuracy in distinguishing LUAD from non-LUAD BMs. This method shows potential for the non-invasive identification of the histological subtype of BMs.

PMID:39552295 | DOI:10.1177/02841851241292528

J Biomol Struct Dyn. 2024 Nov 17:1-17. doi: 10.1080/07391102.2024.2427364. Online ahead of print.

ABSTRACT

Fragments of α-synuclein, an intrinsically disordered protein, whose misfolding and aggregation are responsible for diseases like Parkinson's disease and others, can co-exist in different polymorphs like 'rod' and 'twister'. Their apparently stable structures have different degrees of tolerance to perturbations like point mutations. The molecular basis of this is investigated using molecular dynamics-based conformational sampling. A charge-swapping mutation, E46K, known to be a reason for the early onset of Parkinson's disease, has differential impact on two polymorphs, and its molecular reason has been probed by investigating the intra-fibril interaction network that is responsible for stabilizing the aggregates. Two different quaternary level arrangement of the peptides in two polymorphs, establishing two different types of interrelations between residues of the peptide monomers, form the basis of their differential stabilities; a Deep Neural Network (DNN)-based analysis has extracted different pairs of residues and their spatial proximities as features to distinguish the states of two polymorphs. It has revealed that difference in these molecular arrangements intrinsically assigns key roles to different sets of residues in two different forms, like a feedback loop from quaternary structure to sequence level; an important insight into the sequence-structure relationship in general. Such atomic level insights were substantiated with the proof of differences in the dynamic correlation between residue pairs, altered mobilities of the sidechains that affects packing and redistribution of the weightage of different principal modes of internal motions in different systems. The identification of key residues with altered significance in different polymorphs is likely to benefit the planned design of fibril breaking molecules.

PMID:39552194 | DOI:10.1080/07391102.2024.2427364

Network. 2024 Nov 17:1-41. doi: 10.1080/0954898X.2024.2424248. Online ahead of print.

ABSTRACT

Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.

PMID:39552170 | DOI:10.1080/0954898X.2024.2424248

Brain Behav. 2024 Nov;14(11):e70163. doi: 10.1002/brb3.70163.

ABSTRACT

PURPOSE: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time-consuming sequences while maintaining the image quality.

METHOD: Fully sampled T1-FLAIR, T2-FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects. The T1-FLAIR and T2-FLAIR sequences were subsampled using Cartesian masks based on four different acceleration factors. The fully sampled T1/T2-FLAIR images were predicted from undersampled T1/T2-FLAIR images and T2WI images through deep learning-based reconstruction. They were qualitatively assessed by two senior radiologists in accordance with the diagnosis decision and a four-point scale image quality score. Furthermore, the images were quantitatively assessed based on regional signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs). The chi-square test was performed, where p < 0.05 indicated a difference with statistical significance.

RESULTS: The diagnosis decisions from two senior radiologists remained unchanged in accordance with the accelerated and fully sampled images. There were no significant differences in the regional SNRs and CNRs of most assessed regions (p > 0.05) between the accelerated and fully sampled images. Moreover, no significant difference was identified in the image quality assessed by two senior radiologists (p > 0.05).

CONCLUSION: Deep learning-based image reconstruction is capable of significantly expediting the brain MR imaging process and producing acceptable image quality without affecting diagnosis decisions.

PMID:39552110 | DOI:10.1002/brb3.70163