Optical coherence tomography homography for detection of retinal displacement: a validation study.

PURPOSE: Retinal displacement following rhegmatogenous retinal detachment (RRD) has been associated with inferior functional outcomes. Recent evidence using an overlay technique suggests that fundus-autofluorescence underestimates post-RRD repair retinal displacement. This study aims to validate the overlay technique in normal eyes and to determine its sensitivity and specificity at detecting retinal displacement. METHODS: We conducted a retrospective case series involving 66 normal eyes, each with at least two separate infrared (IR) images at different time points. Overlay of the two images was based on manual marking of choroidal and optic nerve head (ONH) landmarks. For each set of two IR images, computer code for homography generated two outputs, flipping view video and an overlay picture. First, validation of choroidal/ONH alignment was performed using the flipping view video to ensure accurate manual markings. Then, two different masked graders (AB + IM) evaluated the overlays for presence of retinal displacement. 16 control eyes following RRD repair with detected retinal displacement on FAF imaging assessed sensitivity and specificity of the technique. RESULTS: 94% of overlays were found to be well aligned (62/66). 11 cases exhibited errors on flipping view analysis (choroidal/ONH misalignment). Those 11 cases had a significantly higher rate of retinal displacement (false positives) compared to cases without errors (8/11,72% Vs 54/55,98%,P = 0.001). Sensitivity and specificity of the overlay technique for detecting retinal displacement considering only adequate flipping view cases (n = 55) were calculated as 100% and 98%, respectively. CONCLUSIONS: IR overlay emerges as a reliable and valid method for detecting retinal displacement, exhibiting excellent sensitivity and specificity.

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases.

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.

MWIRGas-YOLO: Gas Leakage Detection Based on Mid-Wave Infrared Imaging.

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection.

Research Progress of Cyanine-based Near-Infrared Fluorescent Probes for Biological Application.

Cyanine-based near-infrared (NIR) fluorescent probes have played vital roles in biological application due to their low interference from background fluorescence, deep tissue penetration, high sensitivity, and minimal photodamage to biological samples. They are widely utilized in molecular recognition, medical diagnosis, biomolecular detection, and biological imaging. Herein, we provide a review of recent advancements in cyanine-based NIR fluorescent probes for the detection of pH, cells, tumor as well as their application in photothermal therapy (PTT) and photodynamic therapy (PDT).

Application value of indocyanine green fluorescence imaging in guiding sentinel lymph node biopsy diagnosis of gastric cancer: Meta-analysis.

BACKGROUND: Gastric cancer is a common malignant tumor of the digestive system worldwide, and its early diagnosis is crucial to improve the survival rate of patients. Indocyanine green fluorescence imaging (ICG-FI), as a new imaging technology, has shown potential application prospects in oncology surgery. The meta-analysis to study the application value of ICG-FI in the diagnosis of gastric cancer sentinel lymph node biopsy is helpful to comprehensively evaluate the clinical effect of this technology and provide more reliable guidance for clinical practice. AIM: To assess the diagnostic efficacy of optical imaging in conjunction with indocyanine green (ICG)-guided sentinel lymph node (SLN) biopsy for gastric cancer. METHODS: Electronic databases such as PubMed, Embase, Medline, Web of Science, and the Cochrane Library were searched for prospective diagnostic tests of optical imaging combined with ICG-guided SLN biopsy. Stata 12.0 software was used for analysis by combining the "bivariable mixed effect model" with the "midas" command. The true positive value, false positive value, false negative value, true negative value, and other information from the included literature were extracted. A literature quality assessment map was drawn to describe the overall quality of the included literature. A forest plot was used for heterogeneity analysis, and P < 0.01 was considered to indicate statistical significance. A funnel plot was used to assess publication bias, and P < 0.1 was considered to indicate statistical significance. The summary receiver operating characteristic (SROC) curve was used to calculate the area under the curve (AUC) to determine the diagnostic accuracy. If there was interstudy heterogeneity (I 2 > 50%), meta-regression analysis and subgroup analysis were performed. RESULTS: Optical imaging involves two methods: Near-infrared (NIR) imaging and fluorescence imaging. A combination of optical imaging and ICG-guided SLN biopsy was useful for diagnosis. The positive likelihood ratio was 30.39 (95%CI: 0.92-1.00), the sensitivity was 0.95 (95%CI: 0.82-0.99), and the specificity was 1.00 (95%CI: 0.92-1.00). The negative likelihood ratio was 0.05 (95%CI: 0.01-0.20), the diagnostic odds ratio was 225.54 (95%CI: 88.81-572.77), and the SROC AUC was 1.00 (95%CI: The crucial values were sensitivity = 0.95 (95%CI: 0.82-0.99) and specificity = 1.00 (95%CI: 0.92-1.00). The Deeks method revealed that the "diagnostic odds ratio" funnel plot of SLN biopsy for gastric cancer was significantly asymmetrical (P = 0.01), suggesting significant publication bias. Further meta-subgroup analysis revealed that, compared with fluorescence imaging, NIR imaging had greater sensitivity (0.98 vs 0.73). Compared with optical imaging immediately after ICG injection, optical imaging after 20 minutes obtained greater sensitivity (0.98 vs 0.70). Compared with that of patients with an average SLN detection number < 4, the sensitivity of patients with a SLN detection number ≥ 4 was greater (0.96 vs 0.68). Compared with hematoxylin-eosin (HE) staining, immunohistochemical (+ HE) staining showed greater sensitivity (0.99 vs 0.84). Compared with subserous injection of ICG, submucosal injection achieved greater sensitivity (0.98 vs 0.40). Compared with 5 g/L ICG, 0.5 and 0.05 g/L ICG had greater sensitivity (0.98 vs 0.83), and cT1 stage had greater sensitivity (0.96 vs 0.72) than cT2 to cT3 clinical stage. Compared with that of patients ≤ 26, the sensitivity of patients > 26 was greater (0.96 vs 0.65). Compared with the literature published before 2010, the sensitivity of the literature published after 2010 was greater (0.97 vs 0.81), and the differences were statistically significant (all P < 0.05). CONCLUSION: For the diagnosis of stomach cancer, optical imaging in conjunction with ICG-guided SLN biopsy is a therapeutically viable approach, especially for early gastric cancer. The concentration of ICG used in the SLN biopsy of gastric cancer may be too high. Moreover, NIR imaging is better than fluorescence imaging and may obtain higher sensitivity.

Convolutional neural network advances in demosaicing for fluorescent cancer imaging with color-near-infrared sensors.

Significance: Single-chip imaging devices featuring vertically stacked photodiodes and pixelated spectral filters are advancing multi-dye imaging methods for cancer surgeries, though this innovation comes with a compromise in spatial resolution. To mitigate this drawback, we developed a deep convolutional neural network (CNN) aimed at demosaicing the color and near-infrared (NIR) channels, with its performance validated on both pre-clinical and clinical datasets. Aim: We introduce an optimized deep CNN designed for demosaicing both color and NIR images obtained using a hexachromatic imaging sensor. Approach: A residual CNN was fine-tuned and trained on a dataset of color images and subsequently assessed on a series of dual-channel, color, and NIR images to demonstrate its enhanced performance compared with traditional bilinear interpolation. Results: Our optimized CNN for demosaicing color and NIR images achieves a reduction in the mean square error by 37% for color and 40% for NIR, respectively, and enhances the structural dissimilarity index by 37% across both imaging modalities in pre-clinical data. In clinical datasets, the network improves the mean square error by 35% in color images and 42% in NIR images while enhancing the structural dissimilarity index by 39% in both imaging modalities. Conclusions: We showcase enhancements in image resolution for both color and NIR modalities through the use of an optimized CNN tailored for a hexachromatic image sensor. With the ongoing advancements in graphics card computational power, our approach delivers significant improvements in resolution that are feasible for real-time execution in surgical environments.

INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation.

Infrared (IR) spectroscopic imaging is of potentially wide use in medical imaging applications due to its ability to capture both chemical and spatial information. This complexity of the data both necessitates using machine intelligence as well as presents an opportunity to harness a high-dimensionality data set that offers far more information than today's manually-interpreted images. While convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in image segmentation, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance. In this work, we propose an INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation (INSTRAS). This novel model leverages the strength of the transformer encoders to segment IR breast images effectively. Incorporating skip-connection and transformer encoders, INSTRAS overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. To evaluate the performance of our model and existing convolutional models, we conducted training on various encoder-decoder models using a breast dataset of IR images. INSTRAS, utilizing 9 spectral bands for segmentation, achieved a remarkable AUC score of 0.9788, underscoring its superior capabilities compared to purely convolutional models. These experimental results attest to INSTRAS's advanced and improved segmentation abilities for IR imaging.

Multimodal Imaging Characteristics and Diagnostic Value of Choroidal Nodules in Patients with Neurofibromatosis Type 1.

Objectives: Yasunari nodules are choroidal lesions observed in patients diagnosed with neurofibromatosis type 1 (NF-1) and characterized by relatively irregular dome-shaped, plaque-like, or patchy boundaries. The present study examines the multimodal imaging characteristics of Yasunari nodules and their value in the diagnosis of NF-1. Materials and Methods: Medical records including optical coherence tomography (OCT), enhanced depth imaging OCT, infrared reflectance (IR) imaging, OCT angiography, and color fundus images of NF-1 patients who were examined at the Department of Ophthalmology in Dokuz Eylül University Faculty of Medicine between January 2022 and December 2023 were retrospectively reviewed for the presence of Yasunari nodules. Results: A total of 54 eyes of 27 patients were included in the study. At least one choroidal nodule was detected on IR imaging in 52 eyes (96.3%). In 31 (72.1%) of the 43 eyes (79.6%) with available high-quality OCT angiography images, choroidal nodules were observed as areas showing a flow deficit in the choriocapillaris layer. Of the total 54 eyes included, Lisch nodules without choroidal nodules were observed in 2 eyes (3.7%). In 16 eyes (29.6%), Lisch nodules were not detected despite the presence of choroidal nodules. Both Lisch nodules and choroidal nodules were detected in the other 36 eyes (66.7%). Conclusion: Yasunari nodules are frequently observed in NF-1 cases and can be easily detected with multimodal imaging techniques, especially IR imaging. The ability to visualize choroidal nodules before the appearance of Lisch nodules demonstrates the importance of Yasunari nodules in the diagnosis of NF-1.

Visualized analysis of microplastics in residents' diets and regional investigation of China.

Microplastics are widely distributed in ecosystems and are increasingly found in food. This poses a potential threat to human health. However, current detections of microplastic in food primarily focused on the simple matrices, such as water, milk, and beverages, with relatively few methods available for complex matrices. Due to the strong matrix interference, non-destructive detection of microplastics in food has always been challenging. Thus, in this study, infrared spectral imaging approach was employed in tandem with chemometrics to perform nondestructive and in-situ characterization of microplastics in twelve diverse Chinese diets including meat and seafood stuffs. Results demonstrate that the proposed method can efficiently characterize common microplastics, such as polypropylene (PP), polyethylene terephthalate (PET), and polyethylene (PE), etc., in various complex matrices. The IR spectral imaging was subsequently applied to the detection of microplastics in seafood samples collected from 24 provinces across China. Results revealed the widespread presence of microplastics in seafood diets with significant regional variations. Overall, this study offers an innovative and applicable means for detecting microplastics in complex foods and provides a reference for the rapid detection of microplastics in various materials.

Bilateral acute macular neuroretinopathy associated with COVID-19 infection presenting with central scotoma.

Purpose: To report a case of bilateral acute macular neuroretinopathy (AMN) associated with COVID-19 infection presenting with central scotoma. Observation: A 26-year-old female presented with a chief complaint of bilateral central scotomas for the last seven days. She had a history of fever over the past ten days, and RT-PCR test for COVID-19 was positive on the second day of fever. She had been vaccinated against COVID-19 eight months prior. Her best corrected visual acuity was 6/6 in both eyes on the Snellen chart. Dilated fundus evaluation revealed subtle bilateral perifoveal grey macular lesions. Optical coherence tomography (OCT) demonstrated focal hyperreflectivity at the level of the outer nuclear and plexiform layer consistent with bilateral AMN. Near-infrared reflectance (NIR) and red-free (RF) imaging showed large, confluent hyporeflective lesions in the right eye and discrete petaloid lesions with apices pointing toward the fovea in the left eye. OCT angiography (OCTA) revealed decreased flow signal at the level of the deep capillary plexus (DCP) and choriocapillaris (CC) in both eyes. Automated visual field testing (Humprey Field Analyzer (HFA) 24-2) revealed bilateral central scotoma with depression of adjacent points. After two weeks, the patient had depressed visual fields on HFA 10-2. At two months of final follow-up, OCT macula, NIR and RF images revealed resolving AMN lesions in both eyes. OCTA showed an increase in perfusion at the level of the DCP. There was a decrease in scotoma density on HFA 10-2, suggestive of resolving AMN. Conclusion and importance: AMN with central scotoma as presenting feature of COVID-19 is rare. Fundus findings may be very subtle in AMN, but NIR and RF imaging delineate the lesions well. OCT, NIR imaging, OCTA and HFA 10-2 can be used to assess the clinical course of AMN.

A prospective study on the usefulness of high-resolution intraoperative infrared thermography in intracranial tumors.

Introduction: Infrared thermography (IT) is a non-invasive real-time imaging technique with potential application in different areas of neurosurgery. Despite technological advances in the field, intraoperative IT (IIT) has been an underestimated tool with scarce reports on its usefulness during intracranial tumor resection. We aimed to evaluate the usefulness of high-resolution IIT with static and dynamic thermographic maps for transdural lesion localization, and diagnosis, to assess the extent of resection, and the occurrence of perioperative acute ischemia. Methods: In a prospective study, 15 patients affected by intracranial tumors (six gliomas, four meningiomas, and five brain metastases) were examined with a high-resolution thermographic camera after craniotomy, after dural opening, and at the end of tumor resection. Results: Tumors were transdurally located with 93.3% sensitivity and 100% specificity (p < 0.00001), as well as cortical arteries and veins. Gliomas were consistently hypothermic, while metastases and meningiomas exhibited highly variable thermographic maps on static (p = 0.055) and dynamic (p = 0.015) imaging. Residual tumors revealed non-specific static but characteristic dynamic thermographic maps. Ischemic injuries were significantly hypothermic (p < 0.001). Conclusions: High-resolution IIT is a non-invasive alternative intraoperative imaging method for lesion localization, diagnosis, assessing the extent of tumor resection, and identifying acute ischemia changes with static and dynamic thermographic maps.

Global trends and performances of infrared imaging technology studies on acupuncture: a bibliometric analysis.

Objectives: To summarize development processes and research hotspots of infrared imaging technology research on acupuncture and to provide new insights for researchers in future studies. Methods: Publications regarding infrared imaging technology in acupuncture from 2008 to 2023 were downloaded from the Web of Science Core Collection (WoSCC). VOSviewer 1.6.19, CiteSpace 6.2.R4, Scimago Graphica, and Microsoft Excel software were used for bibliometric analyses. The main analyses include collaboration analyses between countries, institutions, authors, and journals, as well as analyses on keywords and references. Results: A total of 346 publications were retrieved from 2008 to 2023. The quantity of yearly publications increased steadily, with some fluctuations over the past 15 years. "Evidence-Based Complementary and Alternative Medicine" and "American Journal of Chinese Medicine" were the top-cited journals in frequency and centrality. China has the largest number of publications, with the Shanghai University of Traditional Chinese Medicine being the most prolific institution. Among authors, Litscher Gerhard from Austria (currently Swiss University of Traditional Chinese Medicine, Switzerland) in Europe, was the most published and most cited author. The article published by Rojas RF was the most discussed among the cited references. Common keywords included "Acupuncture," "Near infrared spectroscopy," and "Temperature," among others. Explore the relationship between acupoints and temperature through infrared thermography technology (IRT), evaluate pain objectively by functional near-infrared spectroscopy (fNIRS), and explore acupuncture for functional connectivity between brain regions were the hotspots and frontier trends in this field. Conclusion: This study is the first to use bibliometric methods to explore the hotspots and cutting-edge issues in the application of infrared imaging technology in the field of acupuncture. It offers a fresh perspective on infrared imaging technology research on acupuncture and gives scholars useful data to determine the field's hotspots, present state of affairs, and frontier trends.

One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging.

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 µs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

Surface-phonon-polariton-enhanced photoinduced dipole force for nanoscale infrared imaging.

The photoinduced dipole force (PiDF) is an attractive force arising from the Coulombic interaction between the light-induced dipoles on the illuminated tip and the sample. It shows extreme sample-tip distance and refractive index dependence, which is promising for nanoscale infrared (IR) imaging of ultrathin samples. However, the existence of PiDF in the mid-IR region has not been experimentally demonstrated due to the coexistence of photoinduced thermal force (PiTF), typically one to two orders of magnitude higher than PiDF. In this study, we demonstrate that, with the assistance of surface phonon polaritons, the PiDF of c-quartz can be enhanced to surpass its PiTF, enabling a clear observation of PiDF spectra reflecting the properties of the real part of permittivity. Leveraging the detection of the PiDF of phonon polaritonic substrate, we propose a strategy to enhance the sensitivity and contrast of photoinduced force responses in transmission images, facilitating the precise differentiation of the heterogeneous distribution of ultrathin samples.

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics.

Convolutional neural network (CNN) models were devised and evaluated to classify infrared thermal (IRT) images of pediatric wrist fractures. The images were recorded from 19 participants with a wrist fracture and 21 without a fracture (sprain). The injury diagnosis was by X-ray radiography. For each participant, 299 IRT images of their wrists were recorded. These generated 11,960 images (40 participants × 299 images). For each image, the wrist region of interest (ROI) was selected and fast Fourier transformed (FFT) to obtain a magnitude frequency spectrum. The spectrum was resized to 100 × 100 pixels from its center as this region represented the main frequency components. Image augmentations of rotation, translation and shearing were applied to the 11,960 magnitude frequency spectra to assist with the CNN generalization during training. The CNN had 34 layers associated with convolution, batch normalization, rectified linear unit, maximum pooling and SoftMax and classification. The ratio of images for the training and test was 70:30, respectively. The effects of augmentation and dropout on CNN performance were explored. Wrist fracture identification sensitivity and accuracy of 88% and 76%, respectively, were achieved. The CNN model was able to identify wrist fractures; however, a larger sample size would improve accuracy.

Infrared Thermography Monitoring of Durum and Common Wheat for Adaptability Assessing and Yield Performance Prediction.

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p < 0.001 and p < 0.05), providing interesting information for their early prediction.

Near-infrared Absorption and Emission Probes with Optimal Connection Bridges for Live Monitoring of NAD(P)H Dynamics in Living Systems.

Two NAD(P)H-biosensing probes consisting of 1,3,3-trimethyl-3H-indolium and 3-quinolinium acceptors, linked by thiophene, A, and 3,4-ethylenedioxythiophene, B, bridges are detailed. We synthesized probes C and D, replacing the thiophene connection in probe A with phenyl and 2,1,3-benzothiadiazole units, respectively. Probe E was prepared by substituting probe A's 3-quinolinium unit with a 1-methylquinoxalin-1-ium unit. Solutions are non-fluorescent but in the presence of NADH, exhibit near-infrared fluorescence at 742.1 nm and 727.2 nm for probes A and B, respectively, and generate absorbance signals at 690.6 nm and 685.9 nm. In contrast, probes C and D displayed pronounced interference from NADH fluorescence at 450 nm, whereas probe E exhibited minimal fluorescence alterations in response to NAD(P)H. Pre-treatment of A549 cells with glucose in the presence of probe A led to a significant increase in fluorescence intensity. Additionally, subjecting probe A to lactate and pyruvate molecules resulted in opposite changes in NAD(P)H levels, with lactate causing a substantial increase in fluorescence intensity, conversely, pyruvate resulted in a sharp decrease. Treatment of A549 cells with varying concentrations of the drugs cisplatin, gemcitabine, and camptothecin (5, 10, and 20 µM) led to a concentration-dependent increase in intracellular fluorescence intensity, signifying a rise in NAD(P)H levels. Finally, fruit fly larvae were treated with different concentrations of NADH and cisplatin illustrating applicability to live organisms. The results demonstrated a direct correlation between fluorescence intensity and the concentration of NADH and cisplatin, respectively, further confirming the efficacy of probe A in sensing changes in NAD(P)H levels within a whole organism.

A Study on Intelligent Optical Bone Densitometry.

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual's bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

Near-infrared imaging of head and neck squamous cell carcinoma using indocyanine green that targets the αvß6 peptide.

Significance: Head and neck squamous cell carcinoma (HNSCC) has a particularly poor prognosis. Improving the surgical resection boundary, reducing local recurrence, and ultimately ameliorating the overall survival rate are the treatment goals. Aim: To obtain a complete surgical resection (R0 resection), we investigated the use of a fluorescent imaging probe that targets the integrin subtype αvß6, which is upregulated in many kinds of epithelial cancer, using animal models. Approach: αvß6 expression was detected using polymerase chain reaction (PCR) and immunoprotein blotting of human tissues for malignancy. Protein expression localization was observed. αvß6 and epidermal growth factor receptor (EGFR) were quantified by PCR and immunoprotein blotting, and the biosafety of targeting the αvß6 probe material was examined using Cell Counting Kit-8 assays. Indocyanine green (ICG) was used as a control to determine the localization of the probe at the cellular level. In vivo animal experiments were conducted through tail vein injections to evaluate the probe's imaging effect and to confirm its targeting in tissue sections. Results: αvß6 expression was higher than EGFR expression in HNSCC, and the probe showed good targeting in in vivo and in vitro experiments with a good safety profile. Conclusions: The ICG-αvß6 peptide probe is an exceptional and sensitive imaging tool for HNSCC that can distinguish among tumor, normal, and inflammatory tissues.

Vein segmentation and visualization of upper and lower extremities using convolution neural network.

OBJECTIVES: The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments. METHODS: A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization. RESULTS: Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions. CONCLUSIONS: Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.