Significance of Pelvic Fluid Observed during Ovarian Cancer Screening with Transvaginal Sonogram

The primary objective was to examine the role of pelvic fluid observed during transvaginal ultrasonography (TVS) in identifying ovarian malignancy. A single-institution, observational study was conducted within the University of Kentucky Ovarian Cancer Screening trial from January 1987 to September 2019. We analyzed true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) groups for the presence of pelvic fluid during screening encounters. Measured outcomes were the presence and duration of fluid over successive screening encounters. Of the 48,925 women surveyed, 2001 (4.1%) had pelvic fluid present during a TVS exam. The odds ratio (OR) of detecting fluid in the comparison group (TN screen; OR = 1) significantly differed from that of the FP cases (benign pathology; OR: 13.4; 95% confidence interval (CI): 9.1–19.8), the TP cases with a low malignant potential (LMP; OR: 28; 95% CI: 26.5–29.5), TP ovarian cancer cases (OR: 50.4; 95% CI: 27.2–93.2), and FN ovarian cancer cases (OR: 59.3; 95% CI: 19.7–178.1). The mean duration that pelvic fluid was present for women with TN screens was 2.2 ± 0.05 encounters, lasting 38.7 ± 1.3 months. In an asymptomatic screening population, free fluid identified in TVS exams was more associated with ovarian malignancy than in the control group or benign ovarian tumors. While pelvic free fluid may not solely discriminate malignancy from non-malignancy, it appears to be clinically relevant and warrants thoughtful consideration.

Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs

Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively.

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

Diagnostic Value of Endoscopic Ultrasound after Neoadjuvant Chemotherapy for Gastric Cancer Restaging: A Meta-Analysis of Diagnostic Test

This study aimed to evaluate the diagnostic value of endoscopic ultrasound (EUS) after neoadjuvant therapy (NT) for gastric cancer restaging by meta-analysis. We conducted a systematic search of studies published on PubMed and Web of Science up to 30th August 2021. Assessing the risk of bias in the included studies was done with the QUADAS-2 tool. We used R and Review Manager 5.4.1 for calculations and statistical analysis. To evaluate the diagnostic value of EUS after NT for gastric cancer restaging, we performed a meta-analysis on six studies, with a total of 283 patients, including true-positive, true-negative, false-positive, and false-negative results for T1-T4, N0. EUS as a diagnostic test for GC patients after chemotherapy has a relatively low DOR for the T2 (3.96) and T4 stages (4.79) and a relatively high partial AUC for the T2 (0.85) and T4 (0.71) stages. Our results reveal that the pooled sensitivity for T stages after chemotherapy is rather low (29–56%), except for the T3 stage (71%). A potential limitation of our study was the small number of included studies, but no significant heterogeneity was found between them. Our meta-analysis concludes that EUS is not recommended or is still under debate for GC restaging after NT.

Classification of Melanoma and Nevus for Diagnosis of Skin Cancer by Using Optimized Convolution Neural Network

Melanoma is taken into account a fatal sort of carcinoma .Differentiating melanoma from nevus is difficult task. Nevus is a common pigmented skin lesion, usually developing during adulthood, which is harmless. Since they look similar it has to be identified and reduce the risk of cancer. The death rate thanks to this disease is in particular other skin-related consolidated malignancies. In this work, we have used convolution neural networks to classify the image into melanoma and nevus. The images are pre-processed using median filter, top-bottom hat filter and are passed through layers of CNN. We have achieved an accuracy of 97.56%, sensitivity of 95.23%.The F1_socre is 97.56. Index terms: Melanoma, Nevus, True Positive, True Negative, False Negative, False Positive, Confusion Matrix, Epoch, Convolution Neural Network.

Development of a Machine Learning Model for Image-based Email Spam Detection

Combatting email spam has remained a very daunting task. Despite the over 99% accuracy in most non-image-based spam email detection, studies on image-based spam hardly attain such a high level of accuracy as new email spamming techniques that defeat existing spam filters emerges from time to time. The number of email spams sent out daily has remained a key factor in the continued use of spam. In this paper, a simple convolutional neural network model, 123DNet was developed and trained with 28,929 images drawn from 2 public datasets and a Personally Generated dataset. The model was optimized to the least set of layers to have 1 input layer, 2 embedded Convolutional layers as a hidden layer, and 3 neural network layers. The model was tested with a total of 4,339 images of the three dataset samples and then with a separate set of 1,200 images to test performance on never-seen-before images. A Classification Performance analysis was carried out using the confusion matrix. Performance metrics including Accuracy, Precision, True Negative Accuracy, Sensitivity, Specificity, and F1 Measure were computed to ascertain the model’s performance. The Model returned an F1 Score of 97% on a public dataset’s test sample and 88% on Never-seen-before test samples outperforming some pre-existing models while performing significantly well on the newly generated image test samples. It is recommended that a model that performed so well with new never-seen-before spam images be integrated into spam filtering systems. Keywords- Convolutional Neural Network, Deep Learning, Image-based Spam Detection

Investigating Factors of False-Positive Results of Aspergillus Galactomannan Assay: A Case–Control Study in Intensive Care Units

Background: Studies on false-positive galactomannan (GM) enzyme immunoassay (EIA) results and treatment for critically ill patients are scarce.Objectives: The study aimed to determine the false-positive rate of GM-EIA and to probe the risk factors of false positivity among patients in the intensive care units (ICUs).Methods: A case–control approach was conducted to review adult patients who had at least one GM-EIA result and were admitted to the ICU. Those who had no fungal culture were excluded. The clinical characteristics and critical care between patients with false-positive and true-negative GM index (GMI) were compared.Results: Of 206 patients enrolled and with GM-EIA results, 20 (9.7%) were considered to have false-positive antigenemia, including 9 in bronchoalveolar lavages (BAL) and 11 in serum. A total of 148 (71.8%) were true-negatives. After paired grouping of 1:4, factors researched in the previous studies showed no significant difference. However, compared with the true-negatives, patients with positive GM test results but were incompatible with the diagnosis of invasive aspergillosis were more prone to the risk of false positivity due to the use of colistin inhalation. It seemed to be the only factor that significantly increased the risk of false positivity after multivariate analysis (adjusted odds ratio, 35.68; 95% CI, 3.77–337.51, p = 0.002).Conclusions: Colistin inhalation treatment may contribute to false-positive GM-EIA results. The positive GMI among patients receiving colistin nebulization should be interpreted with caution.

Diagnostic Accuracy of MRC in Benign and ML in Obstructive Jaundice Patients, Taking Histopathology as Gold Standard

Objectives: To find out the diagnostic accuracy of magnetic resonance cholangiopancreatography in benign and malignant lesions in patients of obstructive jaundice, taking histopathology as gold standard. Material and methods: This cross sectional study was conducted at Department of Radiology, Bahawal Victoria Hospital, Bahawalpur October 2019 to April 2020. A total of 164 patients with suspected cases of obstructive jaundice and age 25-65 years of either gender were included. Then magnetic resonance cholangiopancreatography (MRCP) was performed in every patient by 1.5 Tesla MR System using a torso phased-array coil. After this, each patient was undergone operation in the concerned ward. Findings of MRCP were compared with the findings of histopathology. Results: In MRCP positive patients, 78 were true positive (TP) and 13 were false positive (FP). Among 73, MRCP negative patients, 08 were false negative (FN) whereas 65 were True Negative (TN). Overall specificity, sensitivity, NPV, PPV and diagnostic accuracy of magnetic resonance cholangiopancreatography in benign and malignant lesions in patients of obstructive jaundice, taking histopathology as gold standard was 90.70%, 83.33%, 85.71%, 89.04% and 87.20% respectively. Conclusion: This study concluded that magnetic resonance cholangiopancreatography (MRCP) is the non-invasive modality of choice with diagnostic accuracy in diagnosing in benign and malignant lesions in obstructive jaundice patients. Keywords: obstructive jaundice, sensitivity, magnetic resonance imaging.

Commitment Without Consequences. A Carnapian Approach to Fictional Objects and Negative Existence Statements

<p>The overall aim of this thesis is to present a fresh perspective on three closely related areas of enquiry: Descriptivist theories of reference, Direct Reference theories and the Carnapian approach to questions of existence and identity. This perspective is developed and tested by a critical analysis of the work of a leading Carnapian theorist, Amie Thomasson, and by looking at some of the central problems associated with our talk of fictional objects. It concludes in an account of negative existence statements and fictional objects as possibly existing objects. In Chapter one I set out the key elements of Carnap’s approach, as that approach was developed over time and in dialogue with his colleague Quine. In Chapter two I explore the relation between the previously mentioned three areas of enquiry through an examination of Amie Thomasson’s brand of Carnapian meta-ontology. In Chapters four and five I develop the view that fictional objects are objects that meet the criteria of existence and identity of at least one linguistic framework but fail to meet the criteria of another, preferred framework. This provides the basis for a neo-Carnapian account of fictional objects in terms of the relations between linguistic frameworks, a novel approach to the questions surrounding such objects. In chapter five, the concluding chapter of the thesis, I further develop my explanation of how there can be truths about fictional and non-existent objects by giving an ontological version of John MacFarlane’s relativity principle. This paves the way for a neo-Carnapian analysis of true negative existence statements. Here I integrate the story I have told about fictional objects and the relations between linguistic frameworks with theories of reference and meaning. In particular, I incorporate a satisfactory concept of the rigid designation of ordinary proper names (and, potentially, of natural and artefactual kind terms). This then leads on to an explanation of how fictional objects, contra Kripke and many others, may reasonably said to be possible objects that, though they don’t exist, might exist under different circumstances.</p>

Commitment Without Consequences. A Carnapian Approach to Fictional Objects and Negative Existence Statements

<p>The overall aim of this thesis is to present a fresh perspective on three closely related areas of enquiry: Descriptivist theories of reference, Direct Reference theories and the Carnapian approach to questions of existence and identity. This perspective is developed and tested by a critical analysis of the work of a leading Carnapian theorist, Amie Thomasson, and by looking at some of the central problems associated with our talk of fictional objects. It concludes in an account of negative existence statements and fictional objects as possibly existing objects. In Chapter one I set out the key elements of Carnap’s approach, as that approach was developed over time and in dialogue with his colleague Quine. In Chapter two I explore the relation between the previously mentioned three areas of enquiry through an examination of Amie Thomasson’s brand of Carnapian meta-ontology. In Chapters four and five I develop the view that fictional objects are objects that meet the criteria of existence and identity of at least one linguistic framework but fail to meet the criteria of another, preferred framework. This provides the basis for a neo-Carnapian account of fictional objects in terms of the relations between linguistic frameworks, a novel approach to the questions surrounding such objects. In chapter five, the concluding chapter of the thesis, I further develop my explanation of how there can be truths about fictional and non-existent objects by giving an ontological version of John MacFarlane’s relativity principle. This paves the way for a neo-Carnapian analysis of true negative existence statements. Here I integrate the story I have told about fictional objects and the relations between linguistic frameworks with theories of reference and meaning. In particular, I incorporate a satisfactory concept of the rigid designation of ordinary proper names (and, potentially, of natural and artefactual kind terms). This then leads on to an explanation of how fictional objects, contra Kripke and many others, may reasonably said to be possible objects that, though they don’t exist, might exist under different circumstances.</p>