Novel locally nebulized indocyanine green for simultaneous identification of tumor margin and intersegmental plane.

BACKGROUND: Segmentectomy, recommended for early-stage lung cancer or compromised lung function, demands precise tumor detection and intersegmental plane identification. While Indocyanine green (ICG) commonly aids in these aspects using near-infrared (NIR) imaging, its separate administrations through different routes and times can lead to complications and patient anxiety. This study aims to develop a lung-specific delivery method by nebulizing low-dose ICG to targeted lung segments, allowing simultaneous detection of lung tumors and intersegmental planes across diverse animal models. METHODS: To optimizing the dose of ICG for lung tumor and interlobar fissure detection, different doses of ICG (0.25, 0.1 and 0.05 mg/kg) were nebulized to rabbit lung tumor models. The distribution of locally nebulized ICG in targeted segments was studied to evaluate the feasibility of detecting lung tumor and intersegmental planes in canine lung pseudotumor models. RESULTS: NIR fluorescence imaging demonstrated clear visualization of lung tumor margin and interlobar fissure using local nebulization of 0.1 mg/kg ICG for only 4 min during surgery in the rabbit models. In the canine model, the local nebulization of 0.05 mg/kg of ICG into the target segment enabled clear visualization of pseudotumor and intersegmental planes for 30 min. CONCLUSIONS: This innovative approach achieves a reduction in ICG dose and prolonged the visualization time of the intersegmental plane and effectively eliminates the need for the hurried marking of tumors and intersegmental planes. We anticipate that lung specific delivery of ICG will prove valuable for image-guided limited resection of lung tumors in clinical practice.

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Purpose: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Methods: Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Results: Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. Conclusions: DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.

An injectable fluorescent and iodinated hydrogel for preoperative localization and dual image-guided surgery of pulmonary nodules.

The widespread use of video-assisted thoracoscopic surgery (VATS) has triggered the rapid expansion in the field of computed tomography (CT)-guided preoperative localization and near-infrared (NIR) fluorescence image-guided surgery. However, its broader application has been hindered by the absence of ideal imaging contrasts that are biocompatible, minimally invasive, highly resolvable, and perfectly localized within the diseased tissue. To achieve this goal, we synthesize a dextran-based fluorescent and iodinated hydrogel, which can be injected into the tissue and imaged with both CT and NIR fluorescence modalities. By finely tuning the physical parameters such as gelation time and composition of iodinated oil (X-ray contrast agent) and indocyanine green (ICG, NIR fluorescence dye), we optimize the hydrogel for prolonged localization at the injected site without losing the dual-imaging capability. We validate the effectiveness of the developed injectable dual-imaging platform by performing image-guided resection of pulmonary nodules on tumor-bearing rabbits, which are preoperatively localized with the hydrogel. The injectable dual-imaging marker, therefore, can emerge as a powerful tool for surgical guidance.

Simultaneous Visualization of Lung Tumor and Intersegmental Plane during Pulmonary Segmentectomy by Intravenous Injection of Indocyanine Green.

Segmentectomy is a targeted surgical approach tailored for patients with compromised health and early-stage lung cancer. The key to successful segmentectomy lies in precisely identifying the tumor and intersegmental planes to ensure adequate resection margins. In this study, we aimed to enhance this process by simultaneously visualizing the tumor and intersegmental planes through the intravenous injection of indocyanine green (ICG) at different time points and doses. Lung tumors were detected by intravenous injection of ICG at a dose of 2 mg/kg 12 h before surgery in a rabbit model. Following the dissection of the pulmonary artery, vein, and bronchi of the target segment, 0.6 mg/kg of ICG was injected intravenously to detect the intersegmental plan. Fluorescent images of the lung tumors and segments were acquired, and the fluorescent signal was quantified using the signal-to-background ratio (SBR). Finally, a pilot study of this method was conducted in three patients with lung cancer. In a preclinical study, the SBR of the tumor (4.4 ± 0.1) and nontargeted segments (10.5 ± 0.8) were significantly higher than that of the targeted segment (1.6 ± 0.2) (targeted segment vs. nontarget segment, p < 0.0001; target segment vs. tumor, p < 0.01). Consistent with preclinical results, lung tumors and the intersegmental plane were successfully detected in patients with lung cancer. Consequently, adequate resection margins were identified during the surgery, and segmentectomy was successfully performed in patients with lung cancer. This study is the first to use intravenous ICG injections at different time points and doses to simultaneously detect lung cancer and intersegmental planes, thereby achieving segmentectomy for lung cancer.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Detection of metastatic lymph node and sentinel lymph node mapping ​using mannose receptor targeting in in vivo mouse footpad tumor models and rabbit uterine cancer models.

BACKGROUND: This study aimed to evaluate the effectiveness of neo-mannosyl human serum albumin-indocyanine green (MSA-ICG) for detecting metastatic lymph node (LN) and mapping sentinel lymph node (SLN) using mouse footpad uterine tumor models. Additionally, the authors assessed the feasibility of MSA-ICG in SLN mapping in rabbit uterine cancer models. MATERIALS AND METHODS: The authors compared the LN targeting ability of MSA-ICG with ICG. Six mouse footpad tumor models and two normal mice were each assigned to MSA-ICG and ICG, respectively. After the assigned tracers were injected, fluorescence images were taken, and the authors compared the signal-to-background ratio (SBR) of the tracers. A SLN biopsy was performed to confirm LN metastasis status and CD206 expression level. Finally, an intraoperative SLN biopsy was performed in rabbit uterine cancer models using MSA-ICG. RESULTS: The authors detected 14 groin LNs out of 16 in the MSA-ICG and ICG groups. The SBR of the MSA-ICG group was significantly higher than that of the ICG group. The metastatic LN subgroup of MSA-ICG showed a significantly higher SBR than that of ICG. CD206 was expressed at a high level in metastatic LN, and the signal intensity difference increased as the CD206 expression level increased. SLN mapping was successfully performed in two of the three rabbit uterine cancer models. CONCLUSIONS: MSA-ICG was able to distinguish metastatic LN for an extended period due to its specific tumor-associated macrophage-targeting property. Therefore, it may be a more distinguishable tracer for identifying metastatic LNs and SLNs during uterine cancer surgery. Further research is needed to confirm these results.

Precise and safe pulmonary segmentectomy enabled by visualizing cancer margins with dual-channel near-infrared fluorescence.

BACKGROUND: Segmentectomy is a type of limited resection surgery indicated for patients with very early-stage lung cancer or compromised function because it can improve quality of life with minimal removal of normal tissue. For segmentectomy, an accurate detection of the tumor with simultaneous identification of the lung intersegment plane is critical. However, it is not easy to identify both during surgery. Here, the authors report dual-channel image-guided lung cancer surgery using renally clearable and physiochemically stable targeted fluorophores to visualize the tumor and intersegmental plane distinctly with different colors; cRGD-ZW800 (800 nm channel) targets tumors specifically, and ZW700 (700 nm channel) simultaneously helps discriminate segmental planes. METHODS: The near-infrared (NIR) fluorophores with 700 nm and with 800 nm channels were developed and evaluated the feasibility of dual-channel fluorescence imaging of lung tumors and intersegmental lines simultaneously in mouse, rabbit, and canine animal models. Expression levels of integrin αvß3, which is targeted by cRGD-ZW800-PEG, were retrospectively studied in the lung tissue of 61 patients who underwent lung cancer surgery. RESULTS: cRGD-ZW800-PEG has clinically useful optical properties and outperforms the FDA-approved NIR fluorophore indocyanine green and serum unstable cRGD-ZW800-1 in multiple animal models of lung cancer. Combined with the blood-pooling agent ZW700-1C, cRGD-ZW800-PEG permits dual-channel NIR fluorescence imaging for intraoperative identification of lung segment lines and tumor margins with different colors simultaneously and accurately. CONCLUSION: This dual-channel image-guided surgery enables complete tumor resection with adequate negative margins that can reduce the recurrence rate and increase the survival rate of lung cancer patients.

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

Anion Engineering for Stabilizing Li Interstitial Sites in Halide Solid Electrolytes for All-Solid-State Li Batteries.

Halide solid electrolytes (SEs) have been highlighted for their high-voltage stability. Among the halide SEs, the ionic conductivity has been improved by aliovalent metal substitutions or choosing a ccp-like anion-arranged monoclinic structure (C2/m) over hcp- or bcc-like anion-arranged structures. Here, we present a new approach, hard-base substitution, and its underlying mechanism to increase the ionic conductivity of halide SEs. The oxygen substitution to Li2ZrCl6 (trigonal, hcp) increased the ionic conductivity from 0.33 to 1.3 mS cm-1 at Li3.1ZrCl4.9O1.1 (monoclinic, ccp), while the sulfur and fluorine substitutions were not effective. A systematic comparison study revealed that the energetic stabilization of interstitial sites for Li migration plays a key role in improving the ionic conductivity, and the ccp-like anion sublattice is not sufficient to achieve high ionic conductivity. We further examined the feasibility of the oxyhalide SE for practical and all-solid-state battery applications.

Automated precision localization of peripherally inserted central catheter tip through model-agnostic multi-stage networks.

BACKGROUND: Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip. OBJECTIVE: This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its misposition. METHODS: To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. Our method consists of the following three stages: 1. Existing PICC line segmentation network for a baseline, 2. Patch-based PICC line refinement network, 3. PICC line reconnection network. The proposed second and third-stage models address MFs caused by the sparseness of the PICC line and the line disconnection due to confusion with anatomical structures respectively, thereby enhancing tip detection. RESULTS: To verify the objective performance of the proposed MFCN, internal validation and external validation were conducted. For internal validation, learning (130 samples) and verification (150 samples) were performed with 280 data, including PICC among Chest X-ray (CXR) images taken at our institution. External validation was conducted using a public dataset called the Royal Australian and New Zealand College of Radiologists (RANZCR), and training (130 samples) and validation (150 samples) were performed with 280 data of CXR images, including PICC, which has the same number as that for internal validation. The performance was compared by root mean squared error (RMSE) and the ratio of single fragment images (RatioSFI) (i.e., the rate at which model predicts PICC as multiple sub-lines) according to whether or not MFCN is applied to seven conventional models (i.e., FCDN, UNET, AUNET, TUNET, FCDN-HT, UNET-ELL, and UNET-RPN). In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45 %. The RMSE improved over 63% from an average of 27.54 mm (17.16 to 35.80 mm) to 9.77 mm (9.11 to 10.98 mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65%. Therefore, by applying the proposed MFCN, we observed the consistent detection performance improvement of PICC tip location compared to the existing model. CONCLUSION: In this study, we applied the proposed technique to the existing technique and demonstrated that it provides high tip detection performance, proving its high versatility and superiority. Therefore, we believe, in countries and regions where radiologists are scarce, that the proposed DL approach will be able to effectively detect PICC misposition on behalf of radiologists.

A general one-pot, solvent-free solid-state synthesis of biocompatible metal nanoparticles using dextran as a tool: Evaluation of their catalytic and anti-cancer activities.

We propose a general green method coupled with a solid-state vibration ball milling strategy for the synthesis of various metal nanoparticles (MNPs), employing a polymeric carbohydrate dextran (Dx) as a reducing and stabilizing molecule. The synthesis of size-controlled Dx-based MNPs (Dx@MNPs), featuring comparatively narrow size distributions, was achieved by controlling the mass ratio of the reactants, reaction time, frequency of the vibration ball mill, and molecular weight of Dx. Notably, this process was conducted at ambient temperatures, without the aid of solvents and accelerating agents, such as NaOH, and conventional reductants as well as stabilizers. Thermal properties of the resulting Dx@MNPs nanocomposites were extensively investigated, highlighting the influence of metal precursors and reaction conditions. Furthermore, the catalytic activity of synthesized nanocomposites was evaluated through the reduction reaction of 4-nitrophenol, exhibiting great catalytic performance. In addition, we demonstrated the excellent biocompatibility of the as-prepared Dx@MNPs toward human embryonic kidney (HEK-293) cells, revealing their potential for anticancer activities. This novel green method for synthesizing biocompatible MNPs with Dx expands the horizons of carbohydrate-based materials as well as MNP nanocomposites for large-scale synthesis and controlled size distribution for various industrial and biomedical applications.

Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of worse prognosis in multiple system atrophy. Recording sounds generated during sleep by video-polysomnography is recommended for detecting stridor, but the analysis is labor intensive and time consuming. A method for automatic stridor detection should be developed using technologies such as artificial intelligence (AI) or machine learning. However, the rarity of stridor hinders the collection of sufficient data from diverse patients. Therefore, an AI method with high diagnostic performance should be devised to address this limitation. We propose an AI method for detecting patients with stridor by combining audio splitting and reintegration with few-shot learning for diagnosis. We used video-polysomnography data from patients with stridor (19 patients with multiple system atrophy) and without stridor (28 patients with parkinsonism and 18 patients with sleep disorders). To the best of our knowledge, this is the first study to propose a method for stridor detection and attempt the validation of few-shot learning to process medical audio signals. Even with a small training set, a substantial improvement was achieved for stridor detection, confirming the clinical utility of our method compared with similar developments. The proposed method achieved a detection accuracy above 96% using data from only eight patients with stridor for training. Performance improvements of 4%-13% were achieved compared with a state-of-the-art AI baseline. Moreover, our method determined whether a patient had stridor and performed real-time localization of the corresponding audio patches, thus providing physicians with support for interpreting and efficiently employing the results of this method.

Optimization of Indocyanine Green for Intraoperative Fluorescent Image-Guided Localization of Lung Cancer; Analysis Based on Solid Component of Lung Nodule.

ICG fluorescence imaging has been used to detect lung cancer; however, there is no consensus regarding the optimization of the indocyanine green (ICG) injection method. The aim of this study was to determine the optimal dose and timing of ICG for lung cancer detection using animal models and to evaluate the feasibility of ICG fluorescence in lung cancer patients. In a preclinical study, twenty C57BL/6 mice with footpad cancer and thirty-three rabbits with VX2 lung cancer were used. These animals received an intravenous injection of ICG at 0.5, 1, 2, or 5 mg/kg, and the cancers were detected using a fluorescent imaging system after 3, 6, 12, and 24 h. In a clinical study, fifty-one patients diagnosed with lung cancer and scheduled to undergo surgery were included. Fluorescent images of lung cancer were obtained, and the fluorescent signal was quantified. Based on a preclinical study, the optimal injection method for lung cancer detection was 2 mg/kg ICG 12 h before surgery. Among the 51 patients, ICG successfully detected 37 of 39 cases with a consolidation-to-tumor (C/T) ratio of >50% (TNR: 3.3 ± 1.2), while it failed in 12 cases with a C/T ratio ≤ 50% and 2 cases with anthracosis. ICG injection at 2 mg/kg, 12 h before surgery was optimal for lung cancer detection. Lung cancers with the C/T ratio > 50% were successfully detected using ICG with a detection rate of 95%, but not with the C/T ratio ≤ 50%. Therefore, further research is needed to develop fluorescent agents targeting lung cancer.

Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus.

BACKGROUND AND OBJECTIVE: The cone-beam computed tomography (CBCT) provides three-dimensional volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. We introduce the development of AI-based computer-aided diagnosis for CBCT considering the intrinsic imaging noise and evaluate its efficacy and implications. METHODS: We propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The proposed method is model agnostic and compatible with various existing and future AI-based denoising or diagnosis models. RESULTS: The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average area under the curve, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. In addition, the physician's ability to evaluate the AI-derived diagnosis results may be enhanced compared with existing solutions. CONCLUSION: This pioneering study on AI-based diagnosis using CBCT indicates that denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions. Furthermore, we believe that the performance enhancement will expedite the adoption of automated diagnostic solutions using CBCT, especially in locations with a shortage of skilled clinicians and limited access to high-dose scanning.

AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems.

BACKGROUND: Compared with chest X-ray (CXR) imaging, which is a single image projected from the front of the patient, chest digital tomosynthesis (CDTS) imaging can be more advantageous for lung lesion detection because it acquires multiple images projected from multiple angles of the patient. Various clinical comparative analysis and verification studies have been reported to demonstrate this, but there is no artificial intelligence (AI)-based comparative analysis studies. Existing AI-based computer-aided detection (CAD) systems for lung lesion diagnosis have been developed mainly based on CXR images; however, CAD-based on CDTS, which uses multi-angle images of patients in various directions, has not been proposed and verified for its usefulness compared to CXR-based counterparts. BACKGROUND AND OBJECTIVE: This study develops and tests a CDTS-based AI CAD system to detect lung lesions to demonstrate performance improvements compared to CXR-based AI CAD. METHODS: We used multiple (e.g., five) projection images as input for the CDTS-based AI model and a single-projection image as input for the CXR-based AI model to compare and evaluate the performance between models. Multiple/single projection input images were obtained by virtual projection on the three-dimensional (3D) stack of computed tomography (CT) slices of each patient's lungs from which the bed area was removed. These multiple images result from shooting from the front and left and right 30/60∘. The projected image captured from the front was used as the input for the CXR-based AI model. The CDTS-based AI model used all five projected images. The proposed CDTS-based AI model consisted of five AI models that received images in each of the five directions, and obtained the final prediction result through an ensemble of five models. Each model used WideResNet-50. To train and evaluate CXR- and CDTS-based AI models, 500 healthy data, 206 tuberculosis data, and 242 pneumonia data were used, and three three-fold cross-validation was applied. RESULTS: The proposed CDTS-based AI CAD system yielded sensitivities of 0.782 and 0.785 and accuracies of 0.895 and 0.837 for the (binary classification) performance of detecting tuberculosis and pneumonia, respectively, against normal subjects. These results show higher performance than the sensitivity of 0.728 and 0.698 and accuracies of 0.874 and 0.826 for detecting tuberculosis and pneumonia through the CXR-based AI CAD, which only uses a single projection image in the frontal direction. We found that CDTS-based AI CAD improved the sensitivity of tuberculosis and pneumonia by 5.4% and 8.7% respectively, compared to CXR-based AI CAD without loss of accuracy. CONCLUSIONS: This study comparatively proves that CDTS-based AI CAD technology can improve performance more than CXR. These results suggest that we can enhance the clinical application of CDTS. Our code is available at https://github.com/kskim-phd/CDTS-CAD-P.

The Ascosphaera apis Infection (Chalkbrood Disease) Alters the Gut Bacteriome Composition of the Honeybee.

The declining honeybee populations are a significant risk to the productivity and security of agriculture worldwide. Although there are many causes of these declines, parasites are a significant one. Disease glitches in honeybees have been identified in recent years and increasing attention has been paid to addressing the issue. Between 30% and 40% of all managed honeybee colonies in the USA have perished annually over the past few years. American foulbrood (AFB) and European foulbrood (EFB) have been reported as bacterial diseases, Nosema as a protozoan disease, and Chalkbrood and Stonebrood as fungal diseases. The study aims to compare the bacterial community related to the Nosema ceranae and Ascosphaera apis infection on the gut of the honeybee and compare it with the weakly active honeybees. The Nosema-infected honeybees contain the phyla Proteobacteria as the significantly dominant bacterial phyla, similar to the weakly active honeybees. In contrast, the Ascosphaera (Chalkbrood) infected honeybee contains large amounts of Firmicutes rather than Proteobacteria.

Identification of Metastatic Lymph Nodes Using Indocyanine Green Fluorescence Imaging.

Indocyanine green (ICG) has been used to detect several types of tumors; however, its ability to detect metastatic lymph nodes (LNs) remains unclear. Our goal was to determine the feasibility of ICG in detecting metastatic LNs. We established a mouse model and evaluated the potential of ICG. The feasibility of detecting metastatic LNs was also evaluated in patients with lung or esophageal cancer, detected with computed tomography (CT) or positron-emission tomography (PET)/CT, and scheduled to undergo surgical resection. Tumors and metastatic LNs were successfully detected in the mice. In the clinical study, the efficacy of ICG was evaluated in 15 tumors and fifty-four LNs with suspected metastasis or anatomically key regional LNs. All 15 tumors were successfully detected. Among the fifty-four LNs, eleven were pathologically confirmed to have metastasis; all eleven were detected in ICG fluorescence imaging, with five in CT and seven in PET/CT. Furthermore, thirty-four LNs with no signals were pathologically confirmed as nonmetastatic. Intravenous injection of ICG may be a useful tool to detect metastatic LNs and tumors. However, ICG is not a targeting agent, and its relatively low fluorescence makes it difficult to use to detect tumors in vivo. Therefore, further studies are needed to develop contrast agents and devices that produce increased fluorescence signals.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes.

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

Beneficial Role of Inherently Formed Residual Lithium Compounds on the Surface of Ni-Rich Cathode Materials for All-Solid-State Batteries.

This study validates the beneficial role of residual Li compounds on the surface of Ni-rich cathode materials (LiNixCoyMnzO2, NCM). Residual Li compounds on Ni-rich NCM are naturally formed during the synthesis procedure, which degrades the initial Coulombic efficiency and generates slurry gelation during electrode fabrication in Li-ion batteries (LIBs) using liquid electrolytes. To solve this problem, washing pretreatment is usually introduced to remove residual Li compounds on the NCM surface. In contrast to LIBs, we found that residual Li compounds can serve as a functional layer that suppresses the interfacial side reactions of the NCM in all-solid-state batteries (ASSBs). The formation of resistive phosphate-based compounds from the undesirable side reaction during the initial charging step is suppressed by the residual Li compounds on the surface of the NCM, thereby reducing polarization growth in ASSBs and enhancing rate performances. The advantageous effects of the intrinsic residual Li compounds on the NCM surface suggest that the essential washing process of the NCM for the liquid-based LIB system should be reconsidered for ASSB systems.