PLoS One. 2024 Dec 3;19(12):e0313126. doi: 10.1371/journal.pone.0313126. eCollection 2024.

ABSTRACT

The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable potential, especially in optimizing radiological evaluations. However, training DL algorithms to clinical standards requires extensive datasets, and their processing is labor-intensive. In this study, we developed an annotation tool named DLLabelsCT that utilizes CNN models to accelerate the image analysis process. To validate DLLabelsCT, we trained a CNN model with a ResNet34 encoder and a UNet decoder to segment the pancreas on an open-access dataset and used the DL model to assist in annotating a local dataset, which was further used to refine the model. DLLabelsCT was also tested on two external testing datasets. The tool accelerates annotation by 3.4 times compared to a completely manual annotation method. Out of 3,715 CT scan slices in the testing datasets, 50% did not require editing when reviewing the segmentations made by the ResNet34-UNet model, and the mean and standard deviation of the Dice similarity coefficient was 0.82±0.24. DLLabelsCT is highly accurate and significantly saves time and resources. Furthermore, it can be easily modified to support other deep learning models for other organs, making it an efficient tool for future research involving larger datasets.

PMID:39625972 | DOI:10.1371/journal.pone.0313126

PLoS One. 2024 Dec 3;19(12):e0312105. doi: 10.1371/journal.pone.0312105. eCollection 2024.

ABSTRACT

In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning, automatic liver tumor segmentation algorithms based on neural networks have emerged, for the improvement of the work efficiency. However, existing liver tumor segmentation algorithms still have several limitations: (1) they often encounter the common issue of class imbalance in liver tumor segmentation tasks, where the tumor region is significantly smaller than the normal tissue region, causing models to predict more negative samples and neglect the tumor region; (2) they fail to adequately consider feature fusion between global contexts, leading to the loss of crucial information; (3) they exhibit weak perception of local details such as fuzzy boundaries, irregular shapes, and small lesions, thereby failing to capture important features. To address these issues, we propose a Multi-Axis Attention Conditional Generative Adversarial Network, referred to as MA-cGAN. Firstly, we propose the Multi-Axis attention mechanism (MA) that projects three-dimensional CT images along different axes to extract two-dimensional features. The features from different axes are then fused by using learnable factors to capture key information from different directions. Secondly, the MA is incorporated into a U-shaped segmentation network as the generator to enhance its ability to extract detailed features. Thirdly, a conditional generative adversarial network is built by combining a discriminator and a generator to enhance the stability and accuracy of the generator's segmentation results. The MA-cGAN was trained and tested on the LiTS public dataset for the liver and tumor segmentation challenge. Experimental results show that MA-cGAN improves the Dice coefficient, Hausdorff distance, average surface distance, and other metrics compared to the state-of-the-art segmentation models. The segmented liver and tumor models have clear edges, fewer false positive regions, and are closer to the true labels, which plays an active role in medical adjuvant therapy. The source code with our proposed model are available at https://github.com/jhliao0525/MA-cGAN.git.

PMID:39625955 | DOI:10.1371/journal.pone.0312105

PLoS One. 2024 Dec 3;19(12):e0313515. doi: 10.1371/journal.pone.0313515. eCollection 2024.

ABSTRACT

Accurate and high coverage genome assemblies are the basis for downstream analysis of metagenomic studies. Long-read sequencing technology is an ideal tool to facilitate the assemblies of metagenome, except for the drawback of usually producing reads with high sequencing error rate. Many polishing tools were developed to correct the sequencing error, but most are designed on the ground of one or two species. Considering the complexity and uneven depth of metagenomic study, we present a novel deep-learning polishing tool named MetaCONNET for polishing metagenomic assemblies. We evaluate MetaCONNET against Medaka, CONNET and NextPolish in accuracy, coverage, contiguity and resource consumption. Our results demonstrate that MetaCONNET provides a valuable polishing tool and can be applied to many metagenomic studies.

PMID:39625881 | DOI:10.1371/journal.pone.0313515

Psychol Res. 2024 Dec 3;89(1):31. doi: 10.1007/s00426-024-02064-2.

ABSTRACT

Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, suggesting that learning and development play a key role in shaping our number sense. This hypothesis is further supported by computational investigations based on deep learning, which have shown that numerosity perception can spontaneously emerge in neural networks that learn the statistical structure of images with a varying number of items. However, neural network models are usually trained using synthetic datasets that might not faithfully reflect the statistical structure of natural environments, and there is also growing interest in using more ecological visual stimuli to investigate numerosity perception in humans. In this work, we exploit recent advances in computer vision algorithms to design and implement an original pipeline that can be used to estimate the distribution of numerosity and non-numerical magnitudes in large-scale datasets containing thousands of real images depicting objects in daily life situations. We show that in natural visual scenes the frequency of appearance of different numerosities follows a power law distribution. Moreover, we show that the correlational structure for numerosity and continuous magnitudes is stable across datasets and scene types (homogeneous vs. heterogeneous object sets). We suggest that considering such "ecological" pattern of covariance is important to understand the influence of non-numerical visual cues on numerosity judgements.

PMID:39625570 | DOI:10.1007/s00426-024-02064-2

Invest Ophthalmol Vis Sci. 2024 Dec 2;65(14):7. doi: 10.1167/iovs.65.14.7.

ABSTRACT

PURPOSE: The purpose of this study was to investigate the effects of physical activity and inactivity on the microvasculature in children, as measured from retinal photographs.

METHODS: All participants were from the Hong Kong Children Eye Study, a population-based cross-sectional study of children aged 6 to 8 years. They received comprehensive ophthalmic examinations and retinal photography. Their demographics and involvement in physical activity and inactivity were obtained from validated questionnaires. A validated Deep Learning System was used to measure, from retinal photographs, central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE).

RESULTS: In the final analysis of 11,959 participants, 6244 (52.2%) were boys and the mean age was 7.55 (1.05) years. Increased ratio of physical activity to inactivity was associated with wider CRAE (β = 1.033, P = 0.007) and narrower CRVE (β = -2.079, P < 0.001). In the subgroup analysis of boys, increased ratio of physical activity to inactivity was associated with wider CRAE (β = 1.364, P = 0.013) and narrower CRVE (β = -2.563, P = 0.001). The subgroup analysis of girls also showed increased ratio of physical activity to inactivity was associated with narrower CRVE (β = -1.759, P = 0.020), but not CRAE.

CONCLUSIONS: Increased activity in children is associated with healthier microvasculature, as shown in the retina. Our study contributes to the growing evidence that physical activity positively influences vascular health from a young age. Therefore, this study also underscores the potential of using the retinal vasculature as a biomarker of cardiovascular health.

PMID:39625440 | DOI:10.1167/iovs.65.14.7

Transl Vis Sci Technol. 2024 Dec 2;13(12):4. doi: 10.1167/tvst.13.12.4.

ABSTRACT

PURPOSE: To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model.

METHODS: Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading.

RESULTS: Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm.

CONCLUSIONS: This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973.

TRANSLATIONAL RELEVANCE: This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.

PMID:39625436 | DOI:10.1167/tvst.13.12.4

Dalton Trans. 2024 Dec 3. doi: 10.1039/d4dt02880e. Online ahead of print.

ABSTRACT

Metal-organic frameworks (MOFs) form a unique platform for operation with data using ReRAM technology. Here we report on a large-scale fabrication of a MOF-based ReRAM array with 6 × 6 cells, demonstrating 50% variation in their electronic parameters. Despite this inhomogeneity, such a "non-ideal" ReRAM array is used for recording binary information followed by deep learning processes to achieve 95% accuracy of reading. Next, the same ReRAM array is used to record numbers (from 0 to 15) followed by the operation of addition. For the correct performance of such an analogue algorithm, we determine a set of unique coefficients for each ReRAM cell, allowing us to use this set as an encrypted key to get access to logical operations. The obtained results, thereby, demonstrate the possibility of "non-ideal" MOF-based ReRAM for low error reading of information coupled with encrypted logical operations.

PMID:39625410 | DOI:10.1039/d4dt02880e

Dermatol Surg. 2024 Dec 3. doi: 10.1097/DSS.0000000000004501. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localization of tumor on Mohs surgery (MMS) frozen section slides, have yet to reach clinical utility.

OBJECTIVE: To train a segmentation model to localize BCC on MMS frozen section slides and to evaluate performance by BCC subtype.

MATERIALS AND METHODS: The study included 348 fresh frozen tissue slides, scanned as whole slide images, from patients treated with MMS for BCC. BCC foci were manually outlined using the Grand Challenge annotation platform. The data set was divided into 80% for training, 10% for validation, and 10% for the test data set. Segmentation was performed using the Ultralytics YOLOv8 model.

RESULTS: Sensitivity was .71 for all tumors, .87 for nodular BCC, .79 for superficial BCC, .74 for micronodular BCC, and .51 for morpheaform and infiltrative BCC. Specificity was .75 for all tumors, .59 for nodular BCC, .58 for superficial BCC, .83 for micronodular BCC, and .74 for morpheaform and infiltrative BCC.

CONCLUSION: This study trained a segmentation model to localize BCC on MMS frozen section slides with reasonably high sensitivity and specificity, and this varied by BCC subtype. More accurate and clinically relevant performance metrics for segmentation studies are needed.

PMID:39625169 | DOI:10.1097/DSS.0000000000004501

Med Phys. 2024 Dec 3. doi: 10.1002/mp.17527. Online ahead of print.

ABSTRACT

BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D-CRT) remains pivotal, influenced by patient anatomy and dosimetric constraints.

PURPOSE: This study aims to develop a decision-making framework utilizing deep learning to predict dose distributions, aiding in the selection of optimal treatment techniques.

METHODS: A 2D U-Net convolutional neural network (CNN) model was used to predict dose distribution maps and dose-volume histogram (DVH) metrics for breast cancer patients undergoing IMRT and 3D-CRT. The model was trained and fine-tuned using retrospective datasets from two medical centers, accounting for variations in CT systems, dosimetric protocols, and clinical practices, over 346 patients. An additional 30 consecutive patients were selected for external validation, where both 3D-CRT and IMRT plans were manually created. To show the potential of the approach, an independent medical physicist evaluated both dosimetric plans and selected the most appropriate one based on applicable clinical criteria. Confusion matrices were used to compare the decisions of the independent observer with the historical decision and the proposed decision-making framework.

RESULTS: Evaluation metrics, including dice similarity coefficients (DSC) and DVH analyses, demonstrated high concordance between predicted and clinical dose distribution for both IMRT and 3D-CRT techniques, especially for organs at risk (OARs). The decision-making framework demonstrated high accuracy (90 % $\%$ ), recall (95.7 % $\%$ ), and precision (91.7 % $\%$ ) when compared to independent clinical evaluations, while the historical decision-making had lower accuracy (50 % $\%$ ), recall (47.8 % $\%$ ), and precision (78.6 % $\%$ ).

CONCLUSIONS: The proposed decision-making model accurately predicts dose distributions for both 3D-CRT and IMRT, ensuring reliable OAR dose estimation. This decision-making framework significantly outperforms historical decision-making, demonstrating higher accuracy, recall, and precision.

PMID:39625151 | DOI:10.1002/mp.17527

Med Phys. 2024 Dec 3. doi: 10.1002/mp.17557. Online ahead of print.

ABSTRACT

BACKGROUND: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction.

PURPOSE: To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT.

METHODS: An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test.

RESULTS: Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4 HU/2.9 HU/40.4 dB/0.82 for CBCTLR, 136.6 HU/-0.4 HU/38.6 dB/0.77 for CBCTSRpro, and 128.2 HU/1.9 HU/39.0 dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66 lp/mm, CBCTSRpro: 0.88 lp/mm, CBCTSRimg: 0.95 lp/mm, CBCTHR: 1.01 lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06 mm for translations (CBCTHR as reference).

CONCLUSIONS: This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

PMID:39625126 | DOI:10.1002/mp.17557

J Appl Clin Med Phys. 2024 Dec 3:e14593. doi: 10.1002/acm2.14593. Online ahead of print.

ABSTRACT

BACKGROUND: Four-dimensional CT is increasingly used for functional cardiac imaging, including prognosis for conditions such as heart failure and post myocardial infarction. However, radiation dose from an acquisition spanning the full cardiac cycle remains a concern. This work investigates the possibility of dose reduction in 4DCT using deep learning (DL)-based segmentation techniques as an objective observer.

METHODS: A 3D residual U-Net was developed for segmentation of left ventricle (LV) myocardium and blood pool. Two networks were trained: Standard DL (trained with only standard-dose [SD] data) and Noise-Robust DL (additionally trained with low-dose data). The primary goal of the proposed DL methods is to serve as an unbiased and consistent observer for functional analysis performance. Functional cardiac metrics including ejection fraction (EF), global longitudinal strain (GLS), circumferential strain (CS), and wall thickness (WT), were measured for an external test set of 250 Cardiac CT volumes reconstructed at five different dose levels.

RESULTS: Functional metrics obtained from DL segmentations of standard dose images matched well with those from expert manual analysis. Utilizing Standard-DL, absolute difference between DL-derived metrics obtained with standard dose data and 100 mA (corresponding to ∼76 ± 13% dose reduction) data was less than 0.8 ± 1.0% for EF, GLS, and CS, and 5.6 ± 6.7% for Average WT. Performance variation of Noise-Robust DL remained acceptable at even 50 mA.

CONCLUSION: We demonstrate that on average radiation dose can be reduced by a factor of 5 while introducing minimal changes to global functional metrics (especially EF, GLS, and CS). The robustness to reduced image quality can be further boosted by using emulated low-dose data in the DL training set.

PMID:39625106 | DOI:10.1002/acm2.14593

Ecol Lett. 2024 Nov;27(11):e70012. doi: 10.1111/ele.70012.

ABSTRACT

Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typically sparse; and (ii) deep-learning models are black-box methods and inferring the processes they represent are non-trivial to elicit. Process-based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process-based models and neural networks into process-informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C-fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process-based models and neural networks, especially in data-sparse regimes with high-transfer task and (ii) to inform on mis- or undetected processes.

PMID:39625058 | DOI:10.1111/ele.70012

Front Pharmacol. 2024 Nov 18;15:1503508. doi: 10.3389/fphar.2024.1503508. eCollection 2024.

ABSTRACT

BACKGROUND: The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000 m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality.

METHODS: To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data.

RESULTS: Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability.

CONCLUSION: This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

PMID:39624838 | PMC:PMC11608951 | DOI:10.3389/fphar.2024.1503508

Ophthalmol Sci. 2024 Aug 23;5(1):100605. doi: 10.1016/j.xops.2024.100605. eCollection 2025 Jan-Feb.

ABSTRACT

PURPOSE: Although structural OCT is traditionally used to differentiate the vascular plexus layers in OCT angiography (OCTA), the vascular plexuses do not always obey the retinal laminations. We sought to segment the superficial, deep, and avascular plexuses from OCTA images using deep learning without structural OCT image input or segmentation boundaries.

DESIGN: Cross-sectional study.

SUBJECTS: The study included 235 OCTA cubes from 33 patients for training and testing of the model.

METHODS: From each OCTA cube, 3 weakly labeled images representing the superficial, deep, and avascular plexuses were obtained for a total of 705 starting images. Images were augmented with standard intensity and geometric transforms, and regions from adjacent plexuses were programmatically combined to create synthetic 2-class images for each OCTA cube. Images were partitioned on a per patient basis into training, validation, and reserved test groups to train and evaluate a U-Net based machine learning model. To investigate the generalization of the model, we applied the model to multiclass thin slabs from OCTA volumes and qualitatively observed the resulting b-scans.

MAIN OUTCOME MEASURES: Plexus segmentation performance was assessed quantitatively using Dice scores on a held-out test set.

RESULTS: After training on single-class plexus images, our model achieved good results (Dice scores > 0.82) and was further improved when using the synthetic 2-class images (Dice >0.95). Although not trained on more complex multiclass slabs, the model performed plexus labeling on slab data, which indicates that the use of only OCTA data shows promise for segmenting the superficial, deep, and avascular plexuses without requiring OCT layer segmentations, and the use of synthetic 2-class images makes a significant performance improvement.

CONCLUSIONS: This study presents the use of OCTA data alone to segment the superficial, deep, and avascular plexuses of the retina, confirming that use of structural OCT layer segmentations as boundaries is not required.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PMID:39624795 | PMC:PMC11609517 | DOI:10.1016/j.xops.2024.100605

Front Bioeng Biotechnol. 2024 Nov 18;12:1500270. doi: 10.3389/fbioe.2024.1500270. eCollection 2024.

ABSTRACT

Deep learning is progressively emerging as a vital tool for image reconstruction in light field microscopy. The present review provides a comprehensive examination of the latest advancements in light field image reconstruction techniques based on deep learning algorithms. First, the review briefly introduced the concept of light field and deep learning techniques. Following that, the application of deep learning in light field image reconstruction was discussed. Subsequently, we classified deep learning-based light field microscopy reconstruction algorithms into three types based on the contribution of deep learning, including fully deep learning-based method, deep learning enhanced raw light field image with numerical inversion volumetric reconstruction, and numerical inversion volumetric reconstruction with deep learning enhanced resolution, and comprehensively analyzed the features of each approach. Finally, we discussed several challenges, including deep neural approaches for increasing the accuracy of light field microscopy to predict temporal information, methods for obtaining light field training data, strategies for data enhancement using existing data, and the interpretability of deep neural networks.

PMID:39624772 | PMC:PMC11608970 | DOI:10.3389/fbioe.2024.1500270

Front Microbiol. 2024 Nov 15;15:1510139. doi: 10.3389/fmicb.2024.1510139. eCollection 2024.

ABSTRACT

INTRODUCTION: The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology.

METHODS: We employed bibliometric analysis and topic modeling to examine 27,420 publications from the Web of Science Core Collection, covering the period from 2010 to 2024. These methods enabled us to identify key trends, research areas, and the geographical distribution of research efforts.

RESULTS: Since 2016, there has been an exponential increase in AI-related publications, with significant contributions from China and the USA. Our analysis identified eight major AI application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management systems. Notably, we found significant lexical overlaps between these areas, especially between drug resistance and vaccine development, suggesting an interconnected research landscape.

DISCUSSION: AI is increasingly moving from laboratory research to clinical applications, enhancing hospital operations and public health strategies. It plays a vital role in optimizing pathogen detection, improving diagnostic speed, treatment efficacy, and disease control, particularly through advancements in rapid antibiotic susceptibility testing and COVID-19 vaccine development. This study highlights the current status, progress, and challenges of AI in pathogenic microbiology, guiding future research directions, resource allocation, and policy-making.

PMID:39624726 | PMC:PMC11610450 | DOI:10.3389/fmicb.2024.1510139

J Bone Oncol. 2024 Nov 9;49:100648. doi: 10.1016/j.jbo.2024.100648. eCollection 2024 Dec.

ABSTRACT

OBJECTIVE: Research on auxiliary diagnosis of primary bone tumors can enhance diagnostic accuracy, facilitate early detection, and enable personalized treatment, thereby reducing misdiagnosis and missed cases, ultimately leading to improved patient prognosis and survival rates. In this study, we established a whole slide imaging (WSI) database comprising histopathological samples from all categories of bone tumors and integrated multiple neural network architectures for machine learning models. We then evaluated the accuracy of these models in diagnosing primary bone tumors.

METHODS: In this paper, the machine learning model based on the deep convolutional neural network (DC-NN) method was combined with imaging omics analysis to analyze and discuss its clinical value in diagnosing primary bone tumors. In addition, this paper proposed a screening method for differentially expressed genes. Based on the paired T-test method, the process first estimated the tumor purity in the experimental data of each sample case, then assessed the actual gene expression value of the experimental data of each sample case, and finally calculated the optimized paired T-test statistics, and screened differentially expressed genes according to the threshold value.

RESULTS: The selected model demonstrated excellent diagnostic accuracy in distinguishing between normal and tumor images, with overall accuracy of (99.8 ± 0.4) % for five rounds of testing using the DCNN model and positive and negative predictive values of (100.0 ± 0.0) % and (99.6 ± 0.8) %, respectively. The mean area under each dataset's curve (AUC) was (0.998 ± 0.004). Further, ten rounds of testing using the DCNN model showed an overall accuracy of (71.2 ± 1.6) % and a substantial positive predictive value of (91.9 ± 8.5) % in distinguishing benign from malignant bone tumors, with an average AUC of (0.62 ± 0.06) across datasets.

CONCLUSION: The deep learning model accurately classifies bone tumor histopathology based on the degree of infiltration, achieving diagnostic performance comparable to that of senior pathologists. These findings affirm the feasibility and effectiveness of histopathological diagnosis in bone tumors, providing a theoretical foundation for the application and advancement of machine learning-assisted histopathological diagnosis in this field.

PMID:39624676 | PMC:PMC11609325 | DOI:10.1016/j.jbo.2024.100648

Front Neurorobot. 2024 Nov 8;18:1484038. doi: 10.3389/fnbot.2024.1484038. eCollection 2024.

ABSTRACT

INTRODUCTION: Precise identification of acupuncture points (acupoints) is essential for effective treatment, but manual location by untrained individuals can often lack accuracy and consistency. This study proposes two approaches that use artificial intelligence (AI) specifically computer vision to automatically and accurately identify acupoints on the face and hand in real-time, enhancing both precision and accessibility in acupuncture practices.

METHODS: The first approach applies a real-time landmark detection system to locate 38 specific acupoints on the face and hand by translating anatomical landmarks from image data into acupoint coordinates. The second approach uses a convolutional neural network (CNN) specifically optimized for pose estimation to detect five key acupoints on the arm and hand (LI11, LI10, TE5, TE3, LI4), drawing on constrained medical imaging data for training. To validate these methods, we compared the predicted acupoint locations with those annotated by experts.

RESULTS: Both approaches demonstrated high accuracy, with mean localization errors of less than 5 mm when compared to expert annotations. The landmark detection system successfully mapped multiple acupoints across the face and hand even in complex imaging scenarios. The data-driven approach accurately detected five arm and hand acupoints with a mean Average Precision (mAP) of 0.99 at OKS 50%.

DISCUSSION: These AI-driven methods establish a solid foundation for the automated localization of acupoints, enhancing both self-guided and professional acupuncture practices. By enabling precise, real-time localization of acupoints, these technologies could improve the accuracy of treatments, facilitate self-training, and increase the accessibility of acupuncture. Future developments could expand these models to include additional acupoints and incorporate them into intuitive applications for broader use.

PMID:39624458 | PMC:PMC11609928 | DOI:10.3389/fnbot.2024.1484038

Phys Imaging Radiat Oncol. 2024 Nov 13;32:100671. doi: 10.1016/j.phro.2024.100671. eCollection 2024 Oct.

ABSTRACT

Adaptive carbon ion radiotherapy for localized prostate cancer requires accurate evaluation of biological dose and dose-averaged linear energy transfer (LETd) changes. This study developed a deep learning model to rapidly predict the modified micro-dosimetric kinetic model (mMKM)-based dose and LETd distributions. Using data from fifty patients for training and testing, the model achieved gamma passing rates exceeding 96% compared to true mMKM-based dose and LETd recalculated from local effect model I (LEM I) plans. Incorporating computed tomography images, contours, physical dose, and LEM I-based dose as inputs, this model provided a rapid, accurate tool for comprehensive evaluations.

PMID:39624391 | PMC:PMC11609691 | DOI:10.1016/j.phro.2024.100671

Heliyon. 2024 Nov 13;10(22):e40350. doi: 10.1016/j.heliyon.2024.e40350. eCollection 2024 Nov 30.

ABSTRACT

Many important information in medical research and clinical diagnosis are obtained from medical images. Among them, digital pathology images can provide detailed tissue structure and cellular information, which has become the gold standard for clinical tumor diagnosis. With the development of neural networks, computer-aided diagnosis presents the identification results of various cell nuclei to doctors, which facilitates the identification of cancerous regions. However, deep learning models require a large amount of annotated data. Pathology images are expensive and difficult to obtain, and insufficient annotation data can easily lead to biased results. In addition, when current models are evaluated on an unknown target domain, there are errors in the predicted boundaries. Based on this, this study proposes a feature alignment-based detail recognition strategy for pathology image segmentation (FASNet). It consists of a preprocessing model and a segmentation network (UNW). The UNW network performs instance normalization and categorical whitening of feature images by inserting semantics-aware normalization and semantics-aware whitening modules into the encoder and decoder, which achieves the compactness of features of the same class and the separation of features of different classes. The FASNet method can identify the feature detail information more efficiently, and thus differentiate between different classes of tissues effectively. The experimental results show that the FASNet method has a Dice Similarity Coefficient (DSC) value of 0.844. It achieves good performance even when faced with test data that does not match the distribution of the training data. Code: https://github.com/zlf010928/FASNet.git.

PMID:39624322 | PMC:PMC11609439 | DOI:10.1016/j.heliyon.2024.e40350