Diagnostics

The aim of this study is to analyze the effectiveness of grayscale intensity histogram to differentiate benign and malignant lesions using a convolutional neural network. Data (200 USG images, 100-malignant, 100-benign) was downloaded from an online access repository. The images were despeckled using ImageJ software and the grayscale intensity histogram values were extracted. In-built neural network pattern recognition application in Matlab R2019b was used to classify the images, which is a two-...

1.<Introduction>Thyroid cancer, or the occurrence of rapid cell growth in the thyroid gland located near the neck, is the fastest growing cancer among women. Papillary thyroid cancer leads to hormonal imbalances thus causing periods of fatigue, difficulty breathing, and an overall decrease in ones quality of life. <Objective>Unsurprisingly, the need for a quick diagnosis of thyroid cancer has become ever more important. Deep learning is a subset of machine learning that may improve the diagnost...

BackgroundThe successful application of deep learning in medical images requires a large amount of annotation data for supervised training. However, massive labeling of medical data is expensive and time consuming. This paper proposes a semi-supervised deep learning method for the detection and classification of benign and malignant breast nodules in ultrasound images, which include two phases. MethodsThe nodule position in the ultrasound image is firstly detected using the faster RCNN network....

Thymic epithelial tumours (TETs) are rare and exhibit varied behaviour and prognosis based on their histological subtype, as classified by the World Health Organization (WHO). These subtypes are further categorized into low-risk and high-risk groups. Low-risk thymomas generally allow for complete surgical resection without adjuvant therapy, while high-risk types often require multimodal treatment due to their aggressive nature. This study aims to evaluate the role of CT radiomics in discriminati...

Breast cancer is a leading malignancy threatening womens health globally, making early and accurate diagnosis crucial. Ultrasound is a key screening and diagnostic tool due to its non-invasive, real-time, and cost-effective nature. However, its diagnostic accuracy is highly dependent on operator experience, and conventional single-image analysis often fails to capture the comprehensive features of a lesion. This study introduces a computer-aided diagnosis (CAD) system that emulates a clinicians ...

BackgroundCurrent methods of COVID-19 detection from other respiratory illnesses using computed tomography (CT) scans are highly inaccurate. However, understanding pathogen-specific immune responses can help reduce inconsistencies and improve the accuracy of COVID-19 and Pneumonia detection. A deep learning model using Relief-based feature selection (RBAs) was developed to detect COVID-19 and Pneumonia. Patient-specific Class Activation Maps (CAMs) were produced to highlight immunopathogenic dif...

Implementing public health diagnostic modalities that are simultaneously accurate and acceptable is integral to effective pandemic response. In this regard, saliva has proven to be a reliable alternative to nasopharyngeal swabs (NPS) for the detection of SARS-COV-2 infections. In particular, the SalivaDirect protocol utilises untreated saliva as its sample type, and removes the need for RNA extraction, thereby decreasing the time and cost of diagnosis by RT-PCR. IN the current study we piloted S...

BackgroundBreast cancer-related lymphedema (BCRL) is a common complication following breast cancer treatment. While lymphoscintigraphy is considered the diagnostic gold standard, it is unsuitable for routine periodic monitoring or assessment of treatment efficacy. Shear wave elastography (SWE) offers a possible alternative, but traditional modes of operation limit its potential. Proposed SolutionsThe Holder-Optimized Elastography (HOE) method is introduced to eliminate pressure issues introduce...

BackgroundNeoadjuvant chemotherapy (NACT) is used to downstage breast cancer prior to surgery. Image monitoring is essential to guide treatment and to assess in vivo chemosensitivity. Breast MRI is considered the gold-standard imaging technique; however, it is contraindicated or poorly tolerated in some patients and may be hard to access. Evidence suggests contrast enhanced spectral mammography (CESM) may approach the accuracy of MRI. This novel pilot study investigates whether the addition of d...

ObjectivesTo assess whether an artificial intelligence (AI) chest radiograph (CXR) tool could enhance lung cancer detection on primary care-referred CXRs in the UK, and to estimate the magnitude of any improvement. MethodsFrom [~]280,000 primary care-referred CXRs, we identified 1,600 linked to a lung cancer diagnosis (ICD-10 C34) within six months. Missed lung cancers were defined by review of the CXR report and comparison of diagnostic CT and positron emission tomography (PET) imaging with th...

PurposeTo investigate and compare the diagnostic performance of ChatGPT 3.5, Google Bard, Microsoft Bing, and two board-certified radiologists in thoracic radiology cases published by The Society of Thoracic Radiology. Materials and MethodsWe collected 124 "Case of the Month" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into ChatGPT 3.5, Google Bard, and Microsoft Bing for diagnosis and differential diagnosi...

1.1)Pneumonia, primarily caused by Streptococcus pneumoniae, and tuberculosis (TB), caused by Mycobacterium tuberculosis, continue to present significant global health challenges. Pneumonia is responsible for 14% of deaths among children under five, resulting in 740,180 fatalities annually [1]. Similarly, TB caused 1.25 million deaths in 2022, including 161,000 among individuals with HIV [2]. Misdiagnosis is a critical issue, with 22.3% of pneumonia cases being misidentified as TB [3], highlight...

ObjectiveTo evaluate the ability of BMRI to detect silicone gel bleeding in a prospective observational study including consecutive patients referred for BMRI scan. Methods: From January 2017 to March 2018, patients referred for BMRI were evaluated in a prospective observational study. Patients who had breast implants were included. BMRI recorded 9 findings according to BI-RADS lexicon and SIGBIC findings, considered equivocal features to detect gel bleeding (GB). Three new original imaging feat...

A convolutional neural network (CNN) was trained and evaluated for detecting suspicious breast lesions on a large dataset of chest CT exams from a teleradiology practice covering over 2,000 hospital sites. Radiologists annotated any discrete nodules or masses appearing within breast tissue, and the model was tested on a held-out set. At a threshold achieving 0.99 specificity, the model demonstrated a sensitivity of 0.32 and a positive predictive value (PPV) of 0.50. In a scenario where sensitivi...

BackgroundThick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images. PurposeTo develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images. Materials and MethodsIn this retrospective study, a de...

IntroductionUntreated urinary tract infections (UTIs) can lead to complications, including renal deterioration due to upper urinary tract involvement. Proteinuria, characterized by excessive protein in the urine, is often indicative of kidney damage. The protein-to-creatinine ratio (P/C ratio) test is a convenient and reliable method for assessing proteinuria. This study aimed to evaluate the urine protein-to-creatinine ratio (UPCR) in UTI patients and its association with renal impairment. Mat...

ObjectivesComparison of three different Artificial intelligence (AI) methods of assessment for patients undergoing Computed tomography (CT) for suspected Covid-19 disease. Parameters studied were probability of diagnosis, quantification of disease severity and the time to reach the diagnosis. Methods107 consecutive patients of suspected Covid-19 patients were evaluated using the three AI methods labeled as Al-I,II, III alongwith visual analysis labeled as VT for predicting probability of Covid-...

Twenty years of research improved the classification of ovarian carcinoma, making the diagnostic relevant from a scientific and clinical perspective. Our research question was to find out if old studies are still pertinent under new diagnostic criteria and how we can use machine learning techniques for re-classification purposes. The same main investigator re-classified 60 cases of ovarian carcinoma after 15 years, using 2014 WHO diagnostic criteria. Selected pathology data only (macro, micro i...

Mammography is the gold standard for breast cancer detection, but there remains a need for supplementary techniques to find lesions it misses. This research explores dynamic farinfrared thermography, which uses a thermal camera to monitor changes in skin temperature over time. The study aims to characterize the thermal behavior of the vascular system, both at rest and in response to a temperature stimulus that causes Vaso modulation (blood vessel constriction or dilation). We use matrix factoriz...

PurposeTo evaluate the potential of using artificial intelligence (AI) focused pulmonary nodule search on chest CT data obtained during the COVID-19 pandemic to identify lung cancer (LC) patients. MethodsA multicenter, retrospective study in the Krasnoyarsk region, Russia analyzed CTs of COVID-19 patients using the automated algorithm, Chest-IRA by IRA Labs. Pulmonary nodules larger than 100 mm3 were identified by the AI and assessed by four radiologists, who categorized them into three groups:...