Computer-aided Image Analysis Research Articles

Continuous specimen cooling during slicing and thickness measurement contributes to improved accuracy in image analysis of pathologic specimens.

Sections were prepared from a paraffin-only block and formalin-fixed paraffin-embedded (FFPE) blocks containing fish sausage and human liver specimens. The ST was compared between the sections prepared with cooling using an ice pack (IP) or a continuous cooling device (CCD) paired with a sliding microtome set at an ST of 4 µm. The sections were stained with eosin or aniline blue, and the association between the percent area of positive staining and ST was determined using computer-aided analysis of images captured with a whole slide scanner. The average STs of the paraffin-only block sections measured by four practitioners were 5.01-5.41 and 4.09-4.33 µm in samples prepared using an IP and a CCD, respectively. Therefore, subsequent analyses included sections prepared using the CCD. The ST of the tissue surface was significantly thinner than that of the paraffin surrounding the tissue section. Furthermore, the percent areas of positive staining for eosin and aniline blue were significantly correlated with ST in both the fish sausage and liver sections. The analysis of the ST and percent area of positive staining in 60 sections of the same block, which were categorized into quantiles based on ST, revealed a significant difference in the percent area of positive staining between the thicker and thinner sections. Specimen sectioning should be performed with a CCD, ST should be measured before the staining of pathologic specimens prepared for quantitative analysis, and histologic examination should be performed using specimens with uniform ST.

Early diagnosis of skin oncologic diseases using artificial intelligence technologies

The last decade has seen significant progress in computer-aided image analysis and recognition, with modern computer-aided diagnostic algorithms not only catching up with, but in many aspects surpassing human abilities. At the heart of this breakthrough is the development of deep convolutional neural networks, which have given a new impetus to medical diagnosis, particularly of skin cancers. In this paper, we analyzed photo-based skin disease classification systems developed using algorithms based on deep learning convolutional neural networks. Such methods have been variously reported to enable automated diagnosis of skin neoplasms with high sensitivity and specificity. A disease that requires more detailed analysis of graphic images — skin melanoma — was chosen as the main object of study. Early diagnosis of melanoma is of great socio-economic importance, as in this case the prognosis of patients is significantly improved. The aim of this work is to analyze the results of artificial intelligence (AI) applications in dermatology, especially in the context of early detection of skin melanoma. Scientific articles were searched in PubMed, Scopus and eLIBRARY databases using the keywords “convolutional neural networks”, “skin cancer” and “artificial intelligence”. The depth of the search was 10 years. The final analysis included 38 sources where the results of our own research were presented. The advantages of artificial intelligence methods for dermatologists were analyzed. Artificial intelligence can significantly assist dermatologists in developing visual neoplasm diagnosis skills and improve diagnostic accuracy. The use of AI to process dermatoscopic data in conjunction with the analysis of anamnestic and clinical information from medical records will reduce the burden on the healthcare system through correctly diagnosed benign skin tumors. All of this promises to have a significant impact on the future development of dermatovenerology.

Standardization of honey as a tissue fixative for histopathology: A morphometric study

Background: Tissue fixation is a crucial step to preserve the tissues in a life-like state with minimal disruption to its cellular and chemical composition for histopathological examination. The search for an effective alternate tissue fixative to the routinely used formaldehyde has gained interest as constant exposure to formaldehyde has proven to be toxic. Honey, an organic substance with high acidity and hygroscopic nature, exhibits tissue fixative properties and has been used in the present study. The present study aimed to standardize honey as a tissue fixative for histopathology by comparing it with formalin. Materials and Methods: In vitro study Oral tissue samples of goat were fixed in 10% honey and 10% formalin solution, respectively, for 24-48 h, followed by routine tissue processing and microscopic examination of 37 slides per group. 2200 epithelial cells (1100 per group) were selected for the computer-aided morphometric image analysis (Fiji-Image J) by three observers. Cell area (CA), cell perimeter (CP), nuclear area (NA), nuclear perimeter (NP), cytoplasmic area (Cyt A), and nuclear-cytoplasmic ratio were the parameters studied. Mann-Whitney U-test (STATA/IC version 16) for inter-group comparison was done and P < 0.05 was considered statistically significant. Results: The probability of epithelial cells in the honey-fixed group to have greater NA, NP, and N/C ratio was about 50%-60%. The probability of epithelial cells in formalin-fixed tissues to have greater CA, CP, and Cyt A was about 70%. Conclusion: Honey is a better nuclear fixative than formalin. Cytoplasmic shrinkage of epithelial cells should be taken into consideration while fixing tissues with honey.

Investigating the Spatial Distribution of Proliferating Cells in Primary Ovarian Cancers.

Ovarian cancer (OC) accounts for about 4% of female cancers globally. While Ki67-immunopositive (Ki67+) cell density is commonly used to assess proliferation in OC, the two-dimensional (2D) distribution pattern of these cells is poorly understood. This study explores the 2D distribution pattern of Ki67+ cells in primary OC tissues and models the proliferation process to improve our understanding of this hallmark of cancer. A total of 100 tissue cores, included in a tissue microarray (TMA) representing 5 clear cell carcinomas, 62 serous carcinomas, 10 mucinous adenocarcinomas, 3 endometrioid adenocarcinomas, 10 lymph node metastases from OC, and 10 samples of adjacent normal ovary tissue, were stained using a standardized immunohistochemical protocol. The computer-aided image analysis system assessed the 2D distribution pattern of Ki67+ proliferating cells, providing the cell number and density, patterns of randomness, and cell-to-cell closeness. Three computer models were created to simulate behavior and responses, aiming to gain insights into the variations in the proliferation process. Significant differences in Ki67+ cell density were found between low- and high-grade serous carcinoma/mucinous adenocarcinomas (p = 0.003 and p = 0.01, respectively). The Nearest Neighbor Index of Ki67+ cells differed significantly between high-grade serous carcinomas and endometrioid adenocarcinomas (p = 0.01), indicating distinct 2D Ki67+ distribution patterns. Proxemics analysis revealed significant differences in Ki67+ cell-to-cell closeness between low- and high-grade serous carcinomas (p = 0.002). Computer models showed varied effects on the overall organization of Ki67+ cells and the ability to preserve the original 2D distribution pattern when altering the location and/or density of Ki67+ cells. Cell proliferation is a hallmark of OCs. This study provides new evidence that investigating the Ki67+ cell density and 2D distribution pattern can assist in understanding the proliferation status of OCs. Moreover, our computer models suggest that changes in Ki67+ cell density and their location are critical for maintaining the 2D distribution pattern.

Processing-microstructure correlations in material extrusion additive manufacturing of carbon fiber reinforced ceramic matrix composites

Liquid silicon infiltration is a cost-effective manufacturing route for carbon fiber reinforced silicon carbide (C/C-SiC)11C/C-SiC: carbon fiber reinforced silicon carbide, which is a ceramic matrix composite with high specific stiffness associated with damage tolerance. To reduce the cost-intensive machining while improving the geometric freedom of design, a processing route for C/C-SiC based on material extrusion additive manufacturing was investigated. Filament-based material extrusion was applied to manufacture green bodies made of short carbon fiber reinforced polyetheretherketone (PEEK)22PEEK: polyetheretherketone. Additionally, a thermo-oxidative crosslinking step, which is assumed to be a function of the specific surface area, was introduced prior to pyrolysis to prevent the re-melting of PEEK. Via computer-aided image analysis, the influence of process parameters during material extrusion on the green bodies’ specific surface area were investigated using statistical design of experiments. It was shown that it is possible to selectively adjust the specific surface area by modifying the microstructure of the carbon fiber reinforced green bodies and hence the shrinkage during pyrolysis, the liquid silicon infiltration kinetics as well as the phase composition of the obtained C/C-SiC. The understanding of the processing-microstructure correlations enabled an improvement of the mechanical properties. With a layer height of 0.1 mm, an infill density of 100% and a printing direction of ± 45°, a flexural strength of 80.3 ± 6.7 MPa was measured.

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Background and ObjectiveDental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.Materials & MethodsThe current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.ResultsThe findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.ConclusionThe GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

Resiliency and Risk Assessment of Smart Vision-Based Skin Screening Applications with Dynamics Modeling

The prevalence of skin diseases remains a concern, leading to a rising demand for the advancement of smart, portable, and non-invasive automated systems and applications. These sought-after technologies allow for the screening of skin lesions through captured images, offering improved and accessible healthcare solutions. Clinical methods include visual inspection by dermatologists; computer-aided vision-based image analysis at healthcare settings; and, lastly, biopsy tests, which are often costly and painful. Given the rise of artificial intelligence-based techniques for image segmentation, analysis, and classification, there remains a need to investigate the resiliency of personalized smartphone (hand-held) skin screening systems with respect to identified risks. This study represents a unique integration of distinct fields pertaining to smart vision-based skin lesion screening, resiliency, risk assessment, and system dynamics. The main focus is to explore the dynamics within the supply chain network of smart skin-lesion-screening systems. With the overarching aim of enhancing health, well-being, and sustainability, this research introduces a new framework designed to evaluate the resiliency of smart skin-lesion-screening applications. The proposed framework incorporates system dynamics modeling within a novel subset of a causal model. It considers the interactions and activities among key factors with unique mapping of capability and vulnerability attributes for effective risk assessment and management. The model has been rigorously tested under various case scenarios and settings. The simulation results offer insights into the model’s dynamics, demonstrating the fact that enhancing the skin screening device/app factors directly improves the resiliency level. Overall, this proposed framework marks an essential step toward comprehending and enhancing the overall resiliency of smart skin-lesion-screening systems.

An Objective Computer-Assisted Measurement of Sonographic Renal Cortical Echogenicity: The Splenorenal Index.

Renal cortical echogenicity represents a marker of renal function. However, evaluation of the renal echotexture is subjective and thus disposed to error and interrater variability. Computer-aided image analysis may be used to objectively assess renal cortical echogenicity by comparing the echogenicity of the left kidney to that of the spleen; the resultant ratio is referred to as the splenorenal index (SRI). We performed a retrospective review of all adult patients who received a renal ultrasound over a 45-day period at our institution. Demographic data and kidney function laboratory values were documented for each patient. Regions of interest (ROIs) were selected in the left renal cortex and spleen using ImageJ software. The SRI was calculated as a ratio of the mean pixel brightness of the left kidney cortex ROI to the mean pixel brightness of the spleen ROI. The SRI was then correlated with serum creatinine, blood urea nitrogen, and estimated glomerular filtration rate. We found that among the 94 patients included in the study, the SRI had a significant positive correlation with serum creatinine ( r = 0.43, P < 0.001) and serum blood urea nitrogen ( r = 0.45, P < 0.001) and negative correlation with estimated glomerular filtration rate ( r = -0.47, P < 0.001). Our data indicate that SRI may serve as a valuable tool for sonographic evaluation of renal parenchymal disease.

Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.

In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.

Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Morphometric Features of Cells of Cervical Intraepithelial Neoplasia Using Computer Aided Image Analysis

Aim: Our goal is to evaluate and compare the different cell morphometric parameters in the various categories of cervical intraepithelial neoplasia (CIN).&#x0D; Study Design: We conducted a cross-sectional study of all cervical intraepithelial neoplasia identified in some areas of biopsies of cervical carcinomas.&#x0D; Place and Duration of Study: The study was in the department of histopathology of LAUTECH Teaching Hospital, Osogbo from January 2006 to December 2015.&#x0D; Materials and Methods: The histologic slides produced were scanned using CS2 APERIO digital slide scanner with standard settings. The scanned slides were then viewed using Qu Path-0.3.2 software. All areas of cervical intraepithelial neoplasia were selected. The perimeter, area, maximum caliper, circularity, eccentricity, and the various measures of hematoxylin optic densities (OD) of the nucleus of the cells were measured. The cytoplasmic morphometric measurements and the nuclear-cell ratio were then estimated.&#x0D; Results: Two cases of CIN 1, one case of CIN 2 and one case of CIN 3 were seen in the background of the eighty-seven cases of cervical carcinomas seen during the study period. A total of 5,935 cells were in the annotated regions with cervical intraepithelial neoplasia. The cells in CIN 1 area are 3,828, CIN 2 area has 1183 cells and CIN 3 areas has 924 cells. Cells of CIN 3 had higher average nuclei hematoxylin optic density (OD), nuclei hematoxylin OD standard deviation and maximum hematoxylin OD than cells of CIN 2 and CIN 1. Cells of CIN 1 have higher average cell area, cell perimeter, cell circularity and mean cytoplasm eosin OD compared to cells of CIN 2 and 3.&#x0D; Conclusion: Computer-aided image analysis using Qu Path software can be very instrumental in differentiating the various categories of cervical intraepithelial neoplasia especially in fragmented biopsies.

Abstract P3-03-25: An Automated Risk Stratification System for Breast Cancer Screening using Thermalytix

Abstract Background: As opposed to conventional age-based population-level breast screening strategies, risk-stratified breast screening programs are emerging as a new approach to balanced population screening methodology where the screening frequency and choice of modality (mammography/tomosynthesis or magnetic resonance imaging) is determined based on accurate personalized estimation of an individual’s risk score. A woman identified with low risk can now be screened less frequently, avoiding repeated mammography screening where radiation risk outweighs the benefits in that particular individual. The standard questionnaire based risk stratification is found to be less reliable and imaging based risk stratification mechanisms are being explored in recent years. In this study, we evaluate the performance of a new computer-aided image analysis technique called Thermalytix that automatically generates a personalized risk score using a combination of imaging and questionnaire information for risk stratification of women. Methodology: Thermalytix is an artificial intelligence system that uses thermal imaging and questionnaire data for predicting the risk of breast cancer. Thermalytix analyzes spatio-thermal signatures and vascular patterns in the breast region along with patients’ complaints and age to generate a score called B-Score (or BHARATI Score) which ranges from 1 to 5. B-Score of 1 indicates low risk of malignancy and a B-Score of 5 indicates the highest risk of malignancy. To evaluate the effectiveness of risk stratification using B-Scores, we performed retrospective analysis of thermal and participants’ data acquired from two registered clinical studies. One study (CTRI/2017/10/0 10 115) is a multi-site study conducted in Bangalore, India, and the other study (NCT04688086) is a single site study conducted in Delhi, India. Both these study sites are geographically distant with 2000 KM apart from each other and comprise a diversified population from India. Results: In total, 717 eligible women were considered in this study with age varying from 18 years to 80 years. Reports from standard of care procedures involving mammography, ultrasound and biopsy (as needed), were collectively considered by a radiologist to determine the ground truth for malignancy. Out of 717 women, 85 women were thus concluded as malignant. When used in a blinded fashion, Thermalytix graded 275 women as B-Score 1 (lowest risk), 225 women as B-Score 2, 44 women as B-Score 3, 137 women as B-Score 4 and 36 women as B-Score 5 (highest risk). The fraction of malignancies in the cohorts corresponding to B-Score categories from 1 to 5 were found to be progressively higher (0.36%, 1.33%, 29.55%, 33.58% and 61.11%, respectively) - showing the correctness of the proposed personalized risk scoring methodology. Conclusion: Thermalytix test, a low-cost, radiation-free, contactless and privacy aware test was used as a technique to determine the breast cancer risk of a woman.. The results obtained in the study show that a high B-score of 5 indicates a high risk for malignancy with 61.11% chance of breast cancer. Likewise the lowest B-Score of 1 indicates low risk for malignancy with just 0.36% percentage of women in the cohort found with malignancy. These results combined with other experiential benefits of Thermalytix test makes it a promising risk stratification mechanism enabling differential frequency of screening while balancing the cost and risk versus benefit. Large scale studies, however, need to be conducted to see the ground benefits of the proposed approach in a screening program implementation. Distribution of study population in different risk cohorts Higher risk correlates with higher malignancy rate Citation Format: Siva Teja Kakileti, Himanshu Madhu, Richa Bansal, Akshita Singh, Sudhakar Sampangi, Bharat Aggarwal, Geetha Manjunath. An Automated Risk Stratification System for Breast Cancer Screening using Thermalytix [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-25.

Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains

PurposeTo develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities. Materials and methodsIn this study, a novel method based on three-dimensional deep autoencoder network is proposed to automatically detect and segment various brain abnormalities without being trained on any abnormal samples. A total of 578 normal T2w MR volumes without obvious abnormalities were used for model training and validation. The proposed 3D autoencoder was evaluated on two different datasets (BraTs dataset and in-house dataset) containing T2w volumes from patients with glioblastoma, multiple sclerosis and cerebral infarction. Lesions detection and segmentation performance were reported as AUC, precision-recall curve, sensitivity, and Dice score. ResultsIn anomaly detection, AUCs for three typical lesions were as follows: glioblastoma, 0.844; multiple sclerosis, 0.858; cerebral infarction, 0.807. In anomaly segmentation, the mean Dice for glioblastomas was 0.462. The proposed network also has the ability to generate an anomaly heatmap for visualization purpose. ConclusionOur proposed method was able to automatically detect various brain anomalies such as glioblastoma, multiple sclerosis, and cerebral infarction. This work suggests that unsupervised anomaly detection is a powerful approach to detect arbitrary brain abnormalities without labeled samples. It has the potential to support diagnostic workflow in radiology as an automated tool for computer-aided image analysis.

AAPM task group report 273: Recommendations on best practices for ai and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both 'traditional' machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support. This article is protected by copyright. All rights reserved.

Deep learning for diagnosis of malign pleural effusion on computed tomography images

BackgroundThe pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called “Pleural Effusion”. Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results. MethodsThe authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test. ResultsAmong the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%). ConclusionAdvances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study.

We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response. Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort. In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7). Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.

Morphometrical Cell Cluster Distribution Pattern Analysis by Computer-Aided Image Analysis Could Distinguish Abnormal Effusion from Normal Effusion Samples

Morphometrical Cell Cluster Distribution Pattern Analysis by Computer-Aided Image Analysis Could Distinguish Abnormal Effusion from Normal Effusion Samples

Review of Deep Learning Approaches for Thyroid Cancer Diagnosis

Thyroid nodule is one of the common life-threatening diseases, and it had an increasing trend over the last years. Ultrasound imaging is a commonly used diagnostic method for detecting and characterizing thyroid nodules. However, assessing the entire slide images is time-consuming and challenging for the experts. For assessing ultrasound images in a meaningful manner, there is a need for automated, trustworthy, and objective approaches. The recent advancements in deep learning have revolutionized many aspects of computer-aided diagnosis (CAD) and image analysis tools that address the problem of diagnosing thyroid nodules. In this study, we explained the objectives of deep learning in thyroid cancer imaging and conducted a literature review on its potential, limits, and current application in this area. We gave an overview of recent progress in thyroid cancer diagnosis using deep learning methods and discussed various challenges and practical problems that might limit the growth of deep learning and its integration into clinical workflow.

Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma.

BACKGROUNDDelays in sentinel lymph node (SLN) biopsy may affect the positivity of non-SLNs. For these reasons, effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision. However, the existing tools, e.g., dermoscopy, do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIMTo investigate the possible association of computer-assisted objectively obtained color, color texture, sharpness and geometry variables with SLN positivity.METHODSWe retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period. The study included patients with histologically confirmed melanomas with Breslow > 0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTSNinety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis. Overall mean (± standard deviation) age was 66 (15) years. The study group consisted of 20 patients with tumor-positive SLN (SLN+) biopsy, who were compared to 79 patients with tumor-negative SLN biopsy specimen (control group). The two groups differed significantly in terms of age (61 years vs 68 years) and histological subtype, with the SLN+ patients being younger and presenting more often with nodular or secondary nodular tumors (P < 0.05). The study group patients showed significantly higher eccentricity (i.e. distance between color and geometrical midpoint) as well as higher sharpness (i.e. these lesions were more discrete from the surrounding normal skin, P < 0.05). Regarding color variables, SLN+ patients demonstrated higher range in all four color intensities (gray, red, green, blue) and significantly higher skewness in three color intensities (gray, red, blue), P < 0.05. Color texture variables, i.e. lacunarity, were comparable in both groups.CONCLUSIONSLN+ patients demonstrated significantly higher eccentricity, higher sharpness, higher range in all four color intensities (gray, red, green, blue) and significantly higher skewness in three color intensities (gray, red, blue). Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+ melanoma patients.