Instant Diagnosis Research Articles

Point-of-care Glycated Hemoglobin (POC-A1c) Testing Helps Early Diagnosis of Diabetes

Just like the DIPSI Guidelines for GDM in India, we must have a rapid screening tool like POC-A1c, which gives an estimate of long-term hyperglycemia and helps early detection. Traditional gold standard methods may delay the diagnosis for an indefinite time, as we have seen in our observations that of those who did not opt for POC-A1c, about 34% did not turn up even after three months. We infer that random capillary blood glucose (with a history of meal timings) of more than 140 mg% using POC-A1c helps in instant diagnosis accuracy of about 93%, or exclusion and necessary action can be taken immediately compared to traditional gold standard methods. It also saves time and is cost-effective as no preparation is required, transportation can be avoided, and it also removes the stress or burden of being positive immediately. We know the important role of POC-A1c in treatment intensification or deintensification in the management of known T2DM patients. We also know that POC-A1c is not recommended for the diagnosis of DM by ADA, but evidence suggests we can opt for RPG and POC-A1c to screen and confirm or exclude T2DM. At the same time, we also recommend large-scale cross sectional studies to recommend POC-A1c as a tool for early screening and diagnosis and definitely reconfirmation is always possible and can be recommended.

Impact of Telemedicine on Blood Glucose Control and Ophthalmic Screenings for Patients with Diabetes in Remote Areas During the COVID-19 Pandemic: A Real-World Study in Northern Taiwan.

Introduction: The purpose of this study was to assess the impact of telemedicine on ophthalmic screening and blood glucose control for patients with diabetes in remote areas of Northern Taiwan during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Telemedicine was implemented in Shiding and Wanli Districts using a 5G platform from April 2021 to December 2022. Patients with poorly controlled diabetes received real-time consultations from endocrinologists at Far Eastern Memorial Hospital, 50 km away, for medication adjustment, diet control, and lifestyle recommendations. The study also provided cloud-upload blood glucose meters for self-monitoring and regular medical advice from hospital nurses. Ophthalmic screenings included fundus imaging, external eye image, and intraocular pressure measurement, with instant communication and diagnosis by ophthalmologists through telemedicine. A satisfaction questionnaire survey was conducted. Results: The study enrolled 196 patients with diabetes. Blood glucose and glycosylated hemoglobin levels were significantly reduced after applying telemedicine (p = 0.01 and p = 0.005, respectively). Ophthalmic screenings led to hospital referrals for 16.0% with abnormal fundus images, 15.6% with severe cataract or anterior segment disorders, and 27.9% with ocular hypertension or glaucoma. Fundus screening rates remained high at 86.3% and 80.4% in 2022, mainly using telemedicine, comparable with the traditional screening rate in the past 5 years. The overall satisfaction rate was 98.5%. Conclusions: Telemedicine showed effectiveness and high satisfaction in managing diabetes and conducting ophthalmic screenings in remote areas during the COVID-19 pandemic. It facilitated early diagnosis and treatment of ocular conditions while maintaining good blood glucose control and fundus screening rates.

A Review on Advancement of Cancer Therapy with Metal Based Nanoparticle

The primary cause of numerous diseases that frequently affect the human body is a compromised immune system. For instance, it can increase the body's susceptibility to infection, which also increases the body's resistance to medicines, by triggering pro-inflammatory reactions and even the loss of good cells and tissues. Metal nanoparticles have the potential to overcome the challenges caused by conventional chemotherapies. Metal nanoparticles play an essential role in the treatment of cancer by targeting, gene suppression, and drug delivery to thetargeted area. The best suitable metal nanoparticles better work with targeted ligands to control the energy deposition within the tumors. Other than therapeutic advantages, metal nanoparticles are also helpful as a diagnostic tool for the imaging of cancerous cells. Moreover, metal nanoparticles provide beneficial options in controlled targeted drug delivery, instant diagnosis, and therapeutic methods. Nanoparticles are useful as therapies and diagnostics because cancer cells are heterogeneous. Breast cancer anti-cancer therapy is difficult due to the disease's aggressive spread to the body. Noble metal nanoparticles are used in the treatment of cancer because of their distinctive features, great biocompatibility, surface effects, and tiny size effects. Numerous cancer treatments have been developed thanks to advances in science and technology, but the global mortality rate for cancer patients is still astronomically high, necessitating conventional treatment approaches in order to preserve lives. Scientists are trying to find a cancer cure, working day and night. Due to their effectiveness and promising outcomes, nanoparticles for the treatment of cancer have acquired relevance.

Intraoperative Flow Cytometry for the Rapid Diagnosis and Validation of Surgical Clearance of Non-Melanoma Skin Cancer: A Prospective Clinical Feasibility Study.

Non-melanoma skin cancer (NMSC) is the most prevalent cancer in humans, with a high global incidence. We present a prospective clinical feasibility study on the use of intraoperative flow cytometry (iFC) for the instant diagnosis of NMSC and its complete surgical clearance. Flow cytometry, a laser-based technique, quantifies cell features, which has applications in cancer research. This study aim is to explore the potential applicability of iFC in detecting and characterizing NMSC and its surgical margins. In total, 30 patients who underwent diagnosis for NMSC were recruited. The method demonstrated high sensitivity (95.2%) and specificity (87.1%), with an accuracy of 91.1%, as confirmed with a receiver operating characteristic curve analysis. The results also indicated that most tumors were diploid, with two cases being hypoploid. The average G0/G1 fractions for normal and tumor tissue samples were 96.03 ± 0.30% and 88.03 ± 1.29%, respectively, with the tumor index escalating from 3.89 ± 0.30% to 11.95 ± 1.29% in cancerous cells. These findings underscore iFC's capability for precise intraoperative NMSC characterization and margin evaluation, promising enhanced complete tumor excision rates. Given the technique's successful application in various other malignancies, its implementation in NMSC diagnosis and treatment holds significant promise and warrants further research in clinical trials.

Instant diagnosis of dementia in older patients who were admitted with forgetfulness to a single geriatric memory clinic

AbstractBackgroundAging is a process associated with progressive and predictable changes that can cause many diseases. As in other systems, structural changes occur in the brain, behavior, and cognitive functions with age. Older people often complain about the decrease in cognitive skills, especially in the memory area and forgetfulness is one of the most common cognitive complaints. In this study, we evaluated the diagnosis of major and minor cognitive impairment in older people who were admitted with forgetfulness to our clinicMethodThis cross‐sectional study, retrospectively desgned, was conducted between January 2018 and January 2022 on a total of 1440 participants who applied to the Geriatrics department with the complaint of forgetfulness, without any acute health problems and with a complete geriatric assessment, including activities of daily living, evaluation of cognitive functions and mood, and blood tests. Dementia diagnosis was made according to the DSM‐V and related specific diagnostic criteria. All demented patients had brain imaging, and cerebrospinal fluid biomarkers, if possible.ResultThe mean age and female frequency were higher in patients with major and minor cognitive impairment while estimated glomerular filtration, folate, and TSH levels were lower. The prevalence of major and minor cognitive impairment was 7.70% and 21.30% respectively. Most of the 3 major neurocognitive impairment diagnoses were Alzheimer’s Disease (7.40%), dementia with Lewy body (3.50%), and Parkinson’s Disease Dementia (1.70%).ConclusionConsidering the risk of dementia in older adults, evaluating the memory complaints has of great importance. This study shows that the majority of the older adults who applied with the complaint of forgetfulness do not have dementia. It is remarkable that the rate of major and minor cognitive impairment was too high to be ruled out in our study population.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography.

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

Design of a high-sensitivity polymer double-slot waveguide sensor for point-of-care biomedical applications

Silicon photonics is a rapidly developing field that offers cost-effective biosensors with improved sensitivity and the potential for interconnecting with other electronic devices for instant disease diagnosis. The Mach Zehnder interferometer architecture (MZI) is a key technology for biosensors, as it detects changes in refractive index (RI) caused by the presence of biomolecules. In this study, a silicon-polymer double-slot waveguide-based MZI was designed, with a small mode area and a large evanescent field to enhance light-analyte interaction. The waveguide was optimized by converting a normal slot waveguide into a double-slot waveguide with varying slot widths. In transmission spectrum, the wavelength shift was measured for both normal and disease samples. Additionally, the loss at a specific wavelength was analyzed to understand the impact of the biomolecule on the sensor performance. The results show that this sensor has a high sensitivity of 2.39 X10^5 nm/RIU, making it a promising candidate for biosensing applications.

Outcome of Percutaneus K Wire Fixation for Paediatric Supracondylar Humerus Fracture

Introduction: Elbow joint is the most common part of upper limb involved in pediatric fractures and 85% of which occur in the distal humerus. In distal humeral fractures, supracondylar humeral fractures are very difficult to treat in the pediatric population.-3Frequently, the mechanism of injury is direct or indirect traumas like falls ,on out stretched hand .This type of fracture requires instant diagnosis and proper management, because any undue delay may complicate the condition and treatment. Methodology: This prospective cross-sectional study was carried out at the department of Orthopaedics Surgery in Dhaka Orthopaedics Hospital, Dhaka, Bangladesh from January 2018 to December, 2019. The Ethical clearance of this study was obtained from the ethics committee of the hospital. A written informed consent was obtained from all the patients (by their parents) and a total of, 18 children aged (5-15) years ,with Grade II and III closed supracondylar fractures of humerus were included in this study. The collected data were analyzed using Statistical Package for Social Sciences (SPSS) software, version-23.0. Chi-square tests were performed to compare the results of the follow up, where p<0.05 considered as the level of significance. Results: A total of 18 children were enrolled in this study. According to sex distribution, 12(67%) children were male and 6(33%) children were female. According to age distribution, 12(67%) children were aged (5-10) years and 6(33%) children were aged (11-15) years. The mean age of the children was 9.16 years. The mode age was 7 years and the median age was 9 years. According to fracture classification, 12 (67%) had Gartland class II and 6(33%) children had Gartland class III. According to mechanism of injury distribution,10(55%) children fall while playing, and followed by 5(28%) fall from the height, 2(11%) children met road traffic accident and 1(6%) children had blunt trauma. On an average, the children aged (5-10) years started .......

Rapid Matching Antibodies Pair and Fast Detecting Melioidosis with Fluorescent Immunochromatographic Test Strips

The high infectivity, difficulty to diagnose, and high drug resistance of melioidosis limited the timeliness of treatment. Lateral flow assay (LFA) was operated in this research to provide an instant diagnosis method, and a fast antibody rapid matching test strategy based on LFA was developed to select the most sensitive and specific pair of antibodies. Compared to the traditional ELISA method, the new matching strategy limits the pairing time to 3 h without any complex instruments. The rapid pairing test strategy is a universal strategy that is suitable for various sandwich immune antigen pairings. To fasten the test of the test strips, dry fluorescence immunoassay analyzer (DFIA) was designed and applied. The equipment also simplifies the process of data acquisition. Finally, the concentration gradient test was operated, and the detection lines and limits were presented.

INDIAMAN-20 (INstant DIAgnosis of 20 Major ANomalies) protocol: application of IOTA diagnostic strategy to fetal anomalies.

To compile a list of instant diagnoses of major fetal anomalies, and to present their sonographic descriptors and test them in a retrospective series of fetuses with congenital anomalies managed at our center. The first step was to identify major anomalies that meet the following criteria: (1) can be diagnosed directly and unequivocally on the basis of a set of descriptors evident on a single sonographic view; (2) are among those more commonly diagnosed in utero; (3) represent major lethal or life-threatening malformations or anomalies for which no or only palliative surgery can be performed. For each of these anomalies, a sonographic descriptor was produced. The second step was to test the descriptors of the instant diagnoses in all cases with these anomalies evaluated since January 2000 in our Fetal Medicine Unit, retrieved from our electronic database. For each congenital anomaly-sonographic descriptor pair we then evaluated the presence vs absence of an unequivocal relationship between anomaly and descriptor, assessing: whether a particular descriptor in a set reference plane identified unequivocally the anomaly, i.e. the same descriptor could not be found in any other malformation; and whether a particular anomaly presented the corresponding descriptor in all cases. Finally, we evaluated whether the sonographic descriptors could be applied in all trimesters or only selectively, considering the natural history of the 20 instant diagnoses. Of the 20 instant diagnoses, five involved the central/peripheral nervous system (exencephaly/anencephaly, alobar/semilobar holoprosencephaly, cephalocele, open spinal dysraphism, complete agenesis of corpus callosum), five involved the heart (hypoplastic left heart syndrome, complete atrioventricular septal defect, severe Ebstein's anomaly, critical aortic stenosis, transposition of the great arteries), five involved the abdominal wall (left-sided congenital diaphragmatic hernia, exomphalos, gastroschisis, body-stalk anomaly) or gastrointestinal tract (duodenal atresia), and five involved the remaining systems (three urinary (autosomal recessive polycystic kidney disease, multicystic dysplastic kidney, lower urinary tract obstruction), two skeletal (transverse limb defect, radial ray defect)). All of the descriptors were found to identify unequivocally the related malformations, whereas four of the malformations were not associated with their reference descriptors in every case. For example, congenital diaphragmatic hernia (left-sided) presented without the stomach in the thorax in 42/93 (45.2%) cases. Due to the natural history of the condition, not all signs were recognizable in the first trimester, some becoming evident only from the second trimester onwards. We have defined sonographic descriptors that enable an unequivocal instant diagnosis for a list of 20 major congenital anomalies: the 'INDIAMAN-20' (INstant DIAgnosis of Major ANomalies) protocol. We believe that using this approach may facilitate focused training of less experienced operators and that it may be incorporated into artificial intelligence and deep learning protocols, further boosting the instant prenatal recognition of these lethal or life-threatening malformations. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Surface Damage Identification for Heritage Site Protection: A Mobile Crowd-sensing Solution Based on Deep Learning

This article addresses the general problem of built heritage protection against both deterioration and loss. To continuously monitor and update the structural health status, a crowd-sensing solution based on powerful and automatic deep learning technique is proposed. The aim of this solution is to get rid of the limitations of manual and visual damage detection methods that are costly and time-consuming. Instead, automatic visual inspection for damage detection on walls is efficiently and effectively performed using an embedded Convolutional Neural Network (CNN). This CNN detects the most frequent types of surface damage on wall photos. The study has been conducted in the Kasbah of Algiers where the four following types of damages have been considered: Efflorescence, spall, crack, and mold. The CNN is designed and trained to be integrated into a mobile application for a participatory crowd-sensing solution. The application should be widely and freely deployed, so any user can take a picture of a suspected damaged wall and get an instant and automatic diagnosis through the embedded CNN. In this context, we have chosen MobileNetV2 with a transfer learning approach. A set of real images have been collected and manually annotated and have been used for training, validation, and test. Extensive experiments have been conducted to assess the efficiency and the effectiveness of the proposed solution, using a 5-fold cross-validation procedure. Obtained results show in particular a mean weighted average precision of 0.868 ± 0.00862 (with a 99% of confidence level) and a mean weighted average recall of 0.84 ± 0.00729 (with a 99% of confidence level). To evaluate the performance of MobileNetV2 as a feature extractor, we conducted a comparative study with other small backbones. Further analysis of CNN activation using Grad-Cam has also been done. Obtained results show that our method remains effective even when using a small network and medium- to low-resolution images. MobileNetV2-based CNN size is smaller, and computational cost better, compared to the other CNNs, with similar performance results. Finally, detected surface damages have also been plotted on a geographic map, giving a global view of their distribution.

A Novel Evaluation Criterion for the Rapid Estimation of the Overcharge and Deep Discharge of Lithium-Ion Batteries Using Differential Capacity

Differential capacity dQ/dU (capacitance) can be used for the instant diagnosis of battery performance in common constant current applications. A novel criterion allows state-of-charge (SOC) and state-of-health (SOH) monitoring of lithium-ion batteries during cycling. Peak values indicate impeding overcharge or deep discharge, while dSOC/dU = dU/dSOC = 1 is close to “full charge” or “empty” and can be used as a marker for SOC = 1 (and SOC = 0) at the instantaneous SOH of the aging battery. Instructions for simple state-of-charge control and fault diagnosis are given.

Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

Differential Capacity as a Tool for SOC and SOH Estimation of Lithium Ion Batteries Using Charge/Discharge Curves, Cyclic Voltammetry, Impedance Spectroscopy, and Heat Events: A Tutorial

State-of-charge (SOC) and state-of-health (SOH) of different cell chemistries were investigated using long-time cycle tests. This practical guide illustrates how differential capacity dQ/dU (capacitance) obtained from discharge curves, impedance spectra, and cyclic voltammograms can be used for the instant diagnosis of lithium-ion batteries without fully charging and discharging the cell. The increase of dU/dQ is an early indicator of upcoming heat events and deep discharge. The criterion dQ/dU = dU/dQ = 1 could serve as an indicator for “full charge”. The frequency response of capacitance correlates with the available charge of the battery and reflects overcharge events and deep discharges long before the battery fails. It is not necessary to measure down to extremely low frequencies because the charge transfer pseudocapacitance of around 10 Hz reflects well the SOC. Computer-aided calculation methods for the evaluation of measurements in industrial environments and for the training of students are presented.

CRISPR/Cas12a-based electrochemical biosensor for highly sensitive detection of cTnI

The successful fabrication of the cTnI detection platform is very meaningful for instant diagnosis of the myocardialinjury and related cardiovascular diseases (CVDs). In this research work, the magnetic nanoparticles and aptamer collaboration with the Cas12a/crRNA are used for the electrochemical detection of cTnI. The aptamer is hybridized with its partially complementary DNA (probe 2, P2) and then is modified on the magnetic nanoparticles. In the presence of cTnI, the cTnI combines with the aptamer and P2 is released. The released P2 is hybridized with the crRNA and the trans-cleavage activity of CRISPR/Cas12a is triggered. Therefore, the methylene blue-modified DNA (probe1, P1) on the surface of the electrode is cleaved, resulting in the decrease of the electrochemical signal. Based on the synergy effect of the high specific target recognition of aptamer, target-specifically triggering trans-cleavage activity of CRISPR/Cas12a, as well as good separation ability of magnetic nanoparticles, the developed electrochemical biosensor enables to detect cTnI with high specificity and sensitivity. The detection limit is low down to 10 pg/mL with a linear range from 100 pg/mL to 50000 pg/mL. The developed sensing platform was successfully applied for the detection of cTnI in human serum. This fabricated CRISPR/Cas12a-based electrochemical biosensor can offer a valuable tool for the diagnosis, prognosis, and treatment of patient with CVDs.

Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

The performance and efficiency of a baler deteriorate as a result of gearbox failure. One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes. This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox. This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction. The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information. Therefore, this method should be capable of timely diagnosis and monitoring. Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained. The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information, 100% fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods. The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

Promoting Students’ Programming Logic and Problem-Solving Awareness With Precision Feedback: A Two-Tier Test-Based Online Programming Training Approach

Fostering students' computer programming skills has become an important educational issue in the globe. However, it remains a challenge for students to understand those abstract concepts when learning computer programming, implying the need to provide instant learning diagnosis and feedback in computer programming activities. In this study, a Two-Tier Test-Based Programming Training (T3PT) approach was proposed. Accordingly, an online learning system was developed to provide students with precision feedback for guiding them to identify misconceptions of computer programming to improve their computer programming learning achievement. In order to examine the effects of the proposed approach, a learning system was developed and a quasi-experiment was conducted. Two classes of 99 eighth-grade students from Taiwan were divided into an experimental group and a control group. The students in the experimental group used the learning system based on the T3PT approach, while the control group used the conventional learning system. The experimental results showed that the proposed approach was significantly superior to the conventional programming learning approach in terms of students' programming logic concepts, problem-solving awareness, technology acceptance, and satisfaction with the learning approach. Accordingly, discussion and suggestions are provided for future research.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks.

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information Technology

Aim:This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays.Background:For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment.Objective:The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis.Methods:Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features.Results:An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making.Conclusion:According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.

Detection of colorectal lesions during colonoscopy.

Owing to its high mortality rate, the prevention of colorectal cancer is of particular importance. The resection of colorectal polyps is reported to drastically reduce colorectal cancer mortality, and examination by endoscopists who had a high adenoma detection rate was found to lower the risk of colorectal cancer, highlighting the importance of identifying lesions. Various devices, imaging techniques, and diagnostic tools aimed at reducing the rate of missed lesions have therefore been developed to improve detection. The distal attachments and devices for improving the endoscopic view angle are intended to help avoid missing blind spots such as folds and flexures in the colon, whereas the imaging techniques represented by image‐enhanced endoscopy contribute to improving lesion visibility. Recent advances in artificial intelligence‐supported detection systems are expected to supplement an endoscopist's eye through the instant diagnosis of the lesions displayed on the monitor. In this review, we provide an outline of each tool and assess its impact on the reduction in the incidence of missed colorectal polyps by summarizing previous clinical research and meta‐analyses. Although useful, the many devices, image‐enhanced endoscopy, and artificial intelligence tools exhibited various limitations. Integrating these tools can improve their shortcomings. Combining artificial intelligence‐based diagnoses with wide‐angle image‐enhanced endoscopy may be particularly useful. Thus, we hope that such tools will be available in the near future.