Analysis and classification of kidney stones based on Raman spectroscopy

Label-free Detection of Urine Extracellular Vesicles from Duchenne Muscular Dystrophy Patients Using Surface-Enhanced Raman Spectroscopy Combined with Machine Learning Models

Duchenne muscular dystrophy (DMD) is a neuromuscular disease that affects males in the pediatric age group. Currently, there is no painless, cost-effective prognostic method available to monitor DMD progression. The main hypothesis of this study was that the biochemical composition of extracellular vesicles (EVs) isolated from the urine of DMD patients can be distinctly differentiated from that of healthy controls using surface-enhanced Raman Spectroscopy (SERS) combined with machine learning models. This differentiation is expected to provide a noninvasive, rapid, and accurate diagnostic tool for the early detection, staging, and monitoring of DMD by identifying the molecular signatures captured by SERS and leveraging the analytical power of machine learning algorithms. We collected fasting morning urine samples from 52 DMD patients and 17 healthy controls and isolated EVs using a Total Exosome Isolation kit. The SERS substrates are prepared using silver nanoparticles, which were employed to capture the molecular fingerprints of the EVs with uniformity and reproducibility, achieving relative standard deviation values of 7.3% and 8.9%. We observed alterations in phenylalanine and α-helical proteins in patients with DMD compared to controls. These spectral data were analyzed using PCA, Support Vector Machines, and k-Nearest Neighbor (KNN) algorithms to identify distinct patterns and stage DMD based on biochemical composition. Our integrated approach demonstrated 60% sensitivity and 100% specificity in distinguishing DMD patients from healthy controls, highlighting the potential of SERS and KNN for noninvasive, accurate, and rapid diagnosis of DMD. This method offers a promising avenue for early detection and personalized treatment strategies, ultimately improving patient outcomes and quality of life.

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness

PURPOSE

Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness.

METHODS

The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed.

RESULTS

Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65-1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods.

CONCLUSION

The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions-diagnosis, monitoring, and treatment-AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

DHM/SERS reveals cellular morphology and molecular changes during iPSCs-derived activation of astrocytes

The activation of astrocytes derived from induced pluripotent stem cells (iPSCs) is of great significance in neuroscience research, and it is crucial to obtain both cellular morphology and biomolecular information non-destructively in situ, which is still complicated by the traditional optical microscopy and biochemical methods such as immunofluorescence and western blot. In this study, we combined digital holographic microscopy (DHM) and surface-enhanced Raman scattering (SERS) to investigate the activation characteristics of iPSCs-derived astrocytes. It was found that the projected area of activated astrocytes decreased by 67%, while the cell dry mass increased by 23%, and the cells changed from a flat polygonal shape to an elongated star-shaped morphology. SERS analysis further revealed an increase in the intensities of protein spectral peaks (phenylalanine 1001 cm-1, proline 1043 cm-1, etc.) and lipid-related peaks (phosphatidylserine 524 cm-1, triglycerides 1264 cm-1, etc.) decreased in intensity. Principal component analysis-linear discriminant analysis (PCA-LDA) modeling based on spectral data distinguished resting and reactive astrocytes with a high accuracy of 96.5%. The increase in dry mass correlated with the increase in protein content, while the decrease in projected area indicated the adjustment of lipid composition and cell membrane remodeling. Importantly, the results not only reveal the cellular morphology and molecular changes during iPSCs-derived astrocytes activation but also reflect their mapping relationship, thereby providing new insights into diagnosing and treating neurodegenerative diseases.

Kidney stone growth through the lens of Raman mapping

Bulk composition of kidney stones, often analyzed with infrared spectroscopy, plays an essential role in determining the course of treatment for kidney stone disease. Though bulk analysis of kidney stones can hint at the general causes of stone formation, it is necessary to understand kidney stone microstructure to further advance potential treatments that rely on in vivo dissolution of stones rather than surgery. The utility of Raman microscopy is demonstrated for the purpose of studying kidney stone microstructure with chemical maps at ≤ 1 µm scales collected for calcium oxalate, calcium phosphate, uric acid, and struvite stones. Observed microstructures are discussed with respect to kidney stone growth and dissolution with emphasis placed on < 5 µm features that would be difficult to identify using alternative techniques including micro computed tomography. These features include thin concentric rings of calcium oxalate monohydrate within uric acid stones and increased frequency of calcium oxalate crystals within regions of elongated crystal growth in a brushite stone. We relate these observations to potential concerns of clinical significance including dissolution of uric acid by raising urine pH and the higher rates of brushite stone recurrence compared to other non-infectious kidney stones.

A novel method for detection of pancreatic Ductal Adenocarcinoma using explainable machine learning

BACKGROUND AND OBJECTIVE

Pancreatic Ductal Adenocarcinoma (PDAC) is a form of pancreatic cancer that is one of the primary causes of cancer-related deaths globally, with less than 10 % of the five years survival rate. The prognosis of pancreatic cancer has remained poor in the last four decades, mainly due to the lack of early diagnostic mechanisms. This study proposes a novel method for detecting PDAC using explainable and supervised machine learning from Raman spectroscopic signals.

METHODS

An insightful feature set consisting of statistical, peak, and extended empirical mode decomposition features is selected using the support vector machine recursive feature elimination method integrated with a correlation bias reduction. Explicable features successfully identified mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumor suppressor protein53 (TP53) in the fingerprint region for the first time in the literature. PDAC and normal pancreas are classified using K-nearest neighbor, linear discriminant analysis, and support vector machine classifiers.

RESULTS

This study achieved a classification accuracy of 98.5% using a nonlinear support vector machine. Our proposed method reduced test time by 28.5 % and saved 85.6 % memory utilization, which reduces complexity significantly and is more accurate than the state-of-the-art method. The generalization of the proposed method is assessed by fifteen-fold cross-validation, and its performance is evaluated using accuracy, specificity, sensitivity, and receiver operating characteristic curves.

CONCLUSIONS

In this study, we proposed a method to detect and define the fingerprint region for PDAC using explainable machine learning. This simple, accurate, and efficient method for PDAC detection in mice could be generalized to examine human pancreatic cancer and provide a basis for precise chemotherapy for early cancer treatment.

Deep-learned generation of renal dual-energy CT from a single-energy scan

AIM

To investigate the role of the deep-learning (DL) method in the generation of dual-energy computed tomography (DECT) images from single-energy images for precise diagnosis of kidney stone type.

MATERIALS AND METHODS

DECT of 23 patients was acquired, and the stone types were investigated based on the DECT software suggestions. The data were divided into two paired groups:120 kVp input and 80 kVp target and 120 kVp input and 135 kVp targets, p2p-UNet-GAN was exploited to generate the different energy images based on the common CT protocols.

RESULTS

The images generated of the generative adversarial network (GAN) network were evaluated based on the SSIM, PSNR, and MSE metrics, and the values were estimated as 0.85-0.95, 28-32, and 0.85-0.89 respectively. The attenuation ratio of test patient images were estimated and compared with real patient reports. The network achieved high accuracy in stone region localisation and resulted in accurate stone type predictions.

CONCLUSION

This study presents a useful method based on the DL technique to reduce patient radiation dose and facilitate the prediction of urinary stone types using single-energy CT imaging.

Spectroscopic investigation of phase transformation of calcium oxalate dehydrates (renal calculi) using acidic Bryophyllum pinnatom powder

Urolithiasis is one of most common renal disorders, characterized by the formation of kidney stones (renal calculi) through the crystallization process within the urinary system. The frequently observed renal calculi are calcium oxalate renal calculi and treatment is done by shock wave method or lithotripsy which is harmful for other cells of the internal system. The objective of this work was to evaluate in vitro diagnosis of calcium oxalate kidney stones in the aqueous solution of Bryophyllum pinnatum. The B. pinnatum powder was mixed in apple cider vinegar and lemon juice separately to make solution 1 and 2 respectively. Apple cider vinegar and lemon juice were used as solvents due to their acidic and body compatible nature. Two surgically removed stones was dipped in solution 1 and 2. After two weeks, kidney stone of weight 2.7 g is completely dissolved in solution 2 while a considerable weight reduction of other kidney stone has been observed in solution 1. Fourier transform infrared (FTIR) spectroscopy results show the presence of two strong absorption peaks at 610 and 912 (cm-1) in both solutions after dissolution of urinary stones are related to calcium oxalate dehydrate (COD). Raman spectra further confirm the dissolution of COD in solution having Raman shifts at 504 and 910 (cm-1). Cluster formation and aggregation of particles has been observed in scanning electron microscopy images. This in vitro study proves that a mixture of Bryophyllum pinnatum powder and lemon juice is a best remedy to remove kidney stones.

Chemical Studies of Multicomponent Kidney Stones Using the Modern Advanced Research Methods

Defining the kidney stone composition is important for determining a treatment plan, understanding etiology and preventing recurrence of nephrolithiasis, which is considered as a common, civilization disease and a serious worldwide medical problem. The aim of this study was to investigate the morphology and chemical composition of multicomponent kidney stones. The identification methods such as infrared spectroscopy (FTIR), X-ray diffraction (XRD), and electron microscopy with the EDX detector were presented. The studies by the X-ray photoelectron spectroscopy (XPS) were also carried out for better understanding of their chemical structure. The chemical mapping by the FTIR microscopy was performed to show the distribution of individual chemical compounds that constitute the building blocks of kidney stones. The use of modern research methods with a particular emphasis on the spectroscopic methods allowed for a thorough examination of the subject of nephrolithiasis.

Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Objective

To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.

Methods

A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.

Results

A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.

Conclusion

AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Identification of Uric Acid Crystals Accumulation in Human and Animal Tissues Using Combined Morphological and Raman Spectroscopy Analysis

Gout is a metabolic condition, common to animals and humans, issuing from the excessive accumulation of end products of proteins degradation. In this study, histopathological and cytological examinations, combined with Raman spectroscopy, have been performed to investigate tissue samples from reptiles, chickens, and humans, presenting lesions produced by uric acid accumulation. As a result of classic processing and staining techniques commonly used in the anatomopathological diagnosis, uric acid crystals lose their structural characteristics, thus making difficult a precise diagnostic. Therefore, complementary diagnostic methods, such as Raman spectroscopy, are needed. This study compares from several perspectives the above mentioned diagnostic methods, concluding that Raman spectroscopy provides highlights in the diagnosis of gout in humans and animals, also adding useful information to differential diagnosis of lesions.

Noninvasive Digital Method for Determining Inflammation after Dental Implantation

This study shows that the luminescent diagnostic of oral fluid allows the determination of the severity of inflammatory markers after implantation. The noninvasive diagnostic method, which is used, allows the rapid detection of the stages of development of the inflammatory process after intraosseous implantation and prevents the development of complications in the postoperative period.

Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review

INTRODUCTION

Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the STREAM-URO framework.

METHODS

Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer.

RESULTS

After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively.

CONCLUSIONS

The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.

Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms

Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.

Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma

Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.

In Situ Identification of Unknown Crystals in Acute Kidney Injury Using Raman Spectroscopy

Raman spectroscopy is a well-established and powerful tool for in situ biomolecular evaluation. Type 2 crystal nephropathies are characterized by the deposition of crystalline materials in the tubular lumen, resulting in rapid onset of acute kidney injury without specific symptoms. Timely crystal identification is essential for its diagnosis, mechanism exploration and therapy, but remains challenging. This study aims to develop a Raman spectroscopy-based method to assist pathological diagnosis of type 2 crystal nephropathies. Unknown crystals in renal tissue slides from a victim suffered extensive burn injury were detected by Raman spectroscopy, and the inclusion of crystals was determined by comparing Raman data with established database. Multiple crystals were scanned to verify the reproducibility of crystal in situ. Raman data of 20 random crystals were obtained, and the distribution and uniformity of substances in crystals were investigated by Raman imaging. A mouse model was established to mimic the crystal nephropathy to verify the availability of Raman spectroscopy in frozen biopsy. All crystals on the human slides were identified to be calcium oxalate dihydrate, and the distribution and content of calcium oxalate dihydrate on a single crystal were uneven. Raman spectroscopy was further validated to be available in identification of calcium oxalate dihydrate crystals in the biopsy specimens. Here, a Raman spectroscopy-based method for in situ identification of unknown crystals in both paraffin-embedded tissues and biopsy specimens was established, providing an effective and promising method to analyze unknown crystals in tissues and assist the precise pathological diagnosis in both clinical and forensic medicine.

Artificial intelligence in endourology: emerging technology for individualized care

PURPOSE OF REVIEW

Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology.

RECENT FINDINGS

Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research.

SUMMARY

Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.

Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to "fingerprint" substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

Purpose of Review

To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist.

Recent Findings

This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it.

Summary

The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

Scale-adaptive Deep Model for Bacterial Raman Spectra Identification

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance.

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.

Human kidney stones: a natural record of universal biomineralization

GeoBioMed - a new transdisciplinary approach that integrates the fields of geology, biology and medicine - reveals that kidney stones composed of calcium-rich minerals precipitate from a continuum of repeated events of crystallization, dissolution and recrystallization that result from the same fundamental natural processes that have governed billions of years of biomineralization on Earth. This contextual change in our understanding of renal stone formation opens fundamentally new avenues of human kidney stone investigation that include analyses of crystalline structure and stratigraphy, diagenetic phase transitions, and paragenetic sequences across broad length scales from hundreds of nanometres to centimetres (five Powers of 10). This paradigm shift has also enabled the development of a new kidney stone classification scheme according to thermodynamic energetics and crystalline architecture. Evidence suggests that ≥50% of the total volume of individual stones have undergone repeated in vivo dissolution and recrystallization. Amorphous calcium phosphate and hydroxyapatite spherules coalesce to form planar concentric zoning and sector zones that indicate disequilibrium precipitation. In addition, calcium oxalate dihydrate and calcium oxalate monohydrate crystal aggregates exhibit high-frequency organic-matter-rich and mineral-rich nanolayering that is orders of magnitude higher than layering observed in analogous coral reef, Roman aqueduct, cave, deep subsurface and hot-spring deposits. This higher frequency nanolayering represents the unique microenvironment of the kidney in which potent crystallization promoters and inhibitors are working in opposition. These GeoBioMed insights identify previously unexplored strategies for development and testing of new clinical therapies for the prevention and treatment of kidney stones.

Studying the Degree of Tooth Enamel Mineralization through Raman Spectroscopy in Various Spectral Ranges

In vitro and in vivo methods of Raman spectroscopy have been developed to assess the degree of mineralization of the enamel of different functional groups. This article presents comparative studies that were carried out using scanning Raman microspectroscopy with various sources of laser excitation with wavelengths of 532, 785, and 1064 nm. It is shown that the intensity of Raman scattering of enamel can be a measure of its thickness. The obtained dependence of the Raman scattering intensity on the distance from the incisal edge is in good agreement with the literature data, where two independent methods (computer tomography and electron microscopy) are used to determine the enamel thickness values. The proposed methods can be considered as potential quantitative methods for express diagnostics of the state of tooth enamel in vivo.

Machine learning for recognizing minerals from multispectral data

Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ML algorithms have been widely used on datasets from individual spectroscopy methods such as vibrational Raman scattering, reflective Visible-Near Infrared (VNIR), and Laser-Induced Breakdown Spectroscopy (LIBS). We firstly reviewed and tested several ML approaches to mineral classification from the existing literature, and identified a novel approach for using Deep Learning algorithms for mineral classification from Raman spectra, that outperform previous state-of-the-art methods. We then developed and evaluated a novel method for automatic mineral identification from combining measurements with two complementary spectroscopic methods using Convolutional Neural Networks (CNN) for Raman and VNIR, and cosine similarity for LIBS. Specifically, we evaluated fusing Raman + VNIR, Raman + LIBS or VNIR + LIBS spectra in order to classify minerals. ML methods applied to combined spectral methods presented here are shown to outperform the use of a single data source by a significant margin. Our approach was tested on both open access experimental Raman (RRUFF) and VNIR (USGS, RELAB, ECOSTRESS) libraries, as well as on synthetic LIBS (NIST) spectral libraries. Our cross-validation tests show that multi-method spectroscopy paired with ML paves the way towards rapid and accurate characterization of rocks and minerals. Future solutions combining Deep Learning Algorithms, together with data fusion from multi-method spectroscopy, could drastically increase the accuracy of automatic mineral recognition compared to existing approaches.

A surface-enhanced Raman scattering-based probe method for detecting chromogranin A in adrenal tumors

Aim: We aim to demonstrate that a surface-enhanced Raman spectroscopy (SERS) probe can be effectively used for protein detection in adrenal tumors. Materials & methods: The SERS probe method, which uses Au@Ag core-shell nanoparticles conjugated with a CgA antibody and a SERS reporter, was applied to detect CgA in adrenal tumors. Results: Our data reveal that the results of the CgA-SERS probe method were almost identical to those of western blot and superior to those of traditional immunohistochemistry. Conclusion: This study offers a novel strategy to detect CgA in adrenal tumors and provides more reliable protein test results than traditional immunohistochemistry analysis for adrenal pathologists, meaning that it might be a better clinical reference for the diagnosis of pheochromocytoma.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Ex vivo detection of cadmium-induced renal damage by using confocal Raman spectroscopy

Cadmium (Cd) is a toxic heavy metal which is harmful to environment and organisms. The reabsorption of Cd in kidney leads it to be the main damaged organ in animals under the Cd exposure. In this work, we applied confocal Raman spectroscopy to map the pathological changes in situ in normal and Cd-exposed mice kidney. The renal tissue from Cd-exposed group displayed a remarkable decreasing in the intensity of typical peaks related to mitochondria, DNA, proteins and lipids. On the contrary, the peaks of collagen in Cd-exposed group elevated significantly. The components in each tissue were identified and distinguished by principal component analysis. Furthermore, all the biological investigations in this study were consistent with the Raman spectrum detection, which revealed the progression and degree of lesion induced by Cd. The confocal Raman spectroscopy provides a new perspective for in situ monitoring of substances changes in tissues, which exhibits more comprehensive understanding of the pathogenic mechanisms of heavy metals in molecular toxicology.