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Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy ( E ap x ) , sample entropy ( E s x ) , Tsallis entropy ( E ts x ) , fuzzy entropy ( E f x ) , Kolmogorov Sinai entropy ( E ks x ) , modified multiscale entropy ( E mms y x ) , permutation entropy ( E p x ) , Renyi entropy ( E r x ) , Shannon entropy ( E sh x ) , wavelet entropy ( E w x ) , signal activity ( S a x ) , Hjorth mobility ( H m x ) , and Hjorth complexity ( H c x ) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina , non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME) . We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.

Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that result in increased mortality, morbidity, healthcare expenditure and decreased quality of life. Electrocardiogram (ECG) is a noninvasive and simple diagnostic method that may demonstrate detectable changes in CHF. However, manual diagnosis of ECG signal is often subject to errors due to the small amplitude and duration of the ECG signals, and in isolation, is neither sensitive nor specific for CHF diagnosis. An automated computer-aided system may enhance the diagnostic objectivity and reliability of ECG signals in CHF. We present an 11-layer deep convolutional neural network (CNN) model for CHF diagnosis herein. This proposed CNN model requires minimum pre-processing of ECG signals, and no engineered features or classification are required. Four different sets of data (A, B, C and D) were used to train and test the proposed CNN model. Out of the four sets, Set B attained the highest accuracy of 98.97%, specificity and sensitivity of 99.01% and 98.87% respectively. The proposed CNN model can be put into practice and serve as a diagnostic aid for cardiologists by providing more objective and faster interpretation of ECG signals.

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

ObjectiveDry eye is a common disease with great health burden and no satisfactory treatment. Traditional Chinese medicine, an increasingly popular form of complementary medicine, has been used to treat dry eye but studies have been inconclusive. To address this issue, we conducted a randomised investigator-masked study which included the robust assessment of disease mechanisms.Methods and analysisEligible participants (total 150) were treated with artificial tear (AT) alone, with added eight sessions of acupuncture (AC) or additional daily oral herb (HB) over a month.ResultsParticipants treated with AC were more likely to respond symptomatically than those on AT (88% vs 72%, p=0.039) with a difference of 16% (95% CI: 0.18 to 31.1). The number-to-treat with AC to achieve response in one person was 7 (3 to 157). Participants in the AC group also had reduced conjunctival redness (automatic grading with Oculus keratograph) compared with AT (p=0.043) and reduced tear T helper cell (Th1)-cytokine tumour necrosis factor α (p=0.027) and Th2-cytokine interleukin 4 concentrations (p=0.038). AC was not significantly superior to AT in other outcomes such as tear osmolarity, tear evaporation rates, corneal staining and tear break-up times. No significant adverse effects were encountered. HB was not significantly different in the primary outcome from AT (80% vs 72%, p=0.26).ConclusionsAC is safe and provides additional benefit in mild to moderate dry eye up to 1 month, compared with ATs alone. Treatment is associated with demonstrable molecular evidence of reduced inflammation. Provided that suitably qualified practitioners are available to implement standardised treatment, AC may be recommended as adjunctive therapy to AT.Trial registration numberClinicalTrials.gov (NCT02219204)registered on 14 August 2014.

Prostate-specific antigen (PSA) testing remains the cornerstone of early prostate cancer detection. Society guidelines for prostate cancer screening via PSA testing serve to standardize patient care and are often used by trainees, junior staff, or generalist medical practitioners to guide medical decision-making. However, adherence to guidelines is a time-consuming and challenging task, and rates of inappropriate PSA testing are high. Retrieval-augmented generation (RAG) is a method to enhance the reliability of large language models (LLMs) by grounding responses in trusted external sources. This study aimed to evaluate a RAG-enhanced LLM system, grounded in current European Association of Urology and American Urological Association guidelines, to assess its effectiveness in providing guideline-concordant PSA screening recommendations compared to junior clinicians. A series of 44 fictional outpatient case scenarios was developed to represent a broad spectrum of clinical presentations. A RAG pipeline was developed, comprising a life expectancy estimation module based on the Charlson Comorbidity Index, followed by LLM-generated recommendations constrained to retrieved excerpts from the European Association of Urology and American Urological Association guidelines. Five junior clinicians were tasked to provide PSA testing recommendations for the same scenarios in closed-book and open-book formats. Answers were compared for accuracy in a binomial fashion. Fleiss κ was computed to assess interrater agreement among clinicians. The RAG-LLM tool provided guideline-concordant recommendations in 95.5% (210/220) of case scenarios, compared to junior clinicians, who were correct in 62.3% (137/220) of scenarios in a closed-book format and 74.1% (163/220) of scenarios in an open-book format. The difference was statistically significant for both closed-book (P<.001) and open-book (P<.001) formats. Interrater agreement among clinicians was fair, with Fleiss κ of 0.294 and 0.321 for closed-book and open-book formats, respectively. Use of RAG techniques allows LLMs to integrate complex guidelines into day-to-day medical decision-making. RAG-LLM tools in urology have the capability to enhance clinical decision-making by providing guideline-concordant recommendations for PSA testing, potentially improving the consistency of health care delivery, reducing cognitive load on clinicians, and reducing unnecessary investigations and costs. While this study used synthetic cases in a controlled simulation environment, it establishes a foundation for future validation in real-world clinical settings.

Thyroid nodules are common, with ultrasound imaging as the primary modality for their assessment. Risk stratification systems like the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) have been developed but suffer from interobserver variability and low specificity. Artificial intelligence, particularly large language models (LLMs) with multimodal capabilities, presents opportunities for efficient end-to-end diagnostic processes. However, their clinical utility remains uncertain. This study evaluates the accuracy and consistency of multimodal LLMs for thyroid nodule risk stratification using the ACR TI-RADS system, examining the effects of model fine-tuning, image annotation, prompt engineering, and comparing open-source versus commercial models. In total, 3 multimodal vision-language models were evaluated: Microsoft's open-source Large Language and Visual Assistant (LLaVA) model, its medically fine-tuned variant (Large Language and Vision Assistant for bioMedicine [LLaVA-Med]), and OpenAI's commercial o3 model. A total of 192 thyroid nodules from publicly available ultrasound image datasets were assessed. Each model was evaluated using 2 prompts (basic and modified) and 2 image scenarios (unlabeled vs radiologist-annotated), yielding 6912 responses. Model outputs were compared with expert ratings for accuracy and consistency. Statistical comparisons included Chi-square tests, Mann-Whitney U tests, and Fleiss' kappa for interrater reliability. Overall, 88.4% (6110/6912) of responses were valid, with the o3 model producing the highest validity rate (2273/2304, 98.6%), followed by LLaVA (2108/2304, 91.5%) and LLaVA-Med (1729/2304, 75%; P<.001). The o3 model demonstrated the highest accuracy overall, achieving up to 57.3% accuracy in Thyroid Imaging Reporting and Data System (TI-RADS) classification, although still remaining suboptimal. Labeled images improved accuracy marginally in nodule margin assessment only when evaluating LLaVA models (407/768, 53% to 447/768, 58.2%; P=.04). Prompt engineering improved accuracy for composition (649/1,152, 56.3% vs 483/1152, 41.9%; P<.001), but significantly reduced accuracy for shape, margins, and overall classification. Consistency was the highest with the o3 model (up to 85.4%), but was comparable for LLaVA and significantly improved with image labeling and modified prompts across multiple TI-RADS categories (P<.001). Subgroup analysis for o3 alone showed prompt engineering did not affect accuracy significantly but markedly improved consistency across all TI-RADS categories (up to 97.1% for shape, P<.001). Interrater reliability was consistently poor across all combinations (Fleiss' kappa<0.60). The study demonstrates the comparative advantages and limitations of multimodal LLMs for thyroid nodule risk stratification. While the commercial model (o3) consistently outperformed open-source models in accuracy and consistency, even the best-performing model outputs remained suboptimal for direct clinical deployment. Prompt engineering significantly enhanced output consistency, particularly in the commercial model. These findings underline the importance of strategic model optimization techniques and highlight areas requiring further development before multimodal LLMs can be reliably used in clinical thyroid imaging workflows.

IntroductionLarge learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically relevant task is scoring the Boston Bowel Preparation Scale (BBPS). While traditional AI techniques use large amounts of data for training, we hypothesise that vision-language LLM can perform this task with fewer examples.MethodsWe used the GPT4V vision-language LLM developed by OpenAI, via the OpenAI application programming interface. A standardised prompt instructed the model to grade BBPS with contextual references extracted from the original paper describing the BBPS by Lai et al (GIE 2009). Performance was tested on the HyperKvasir dataset, an open dataset for automated BBPS grading.ResultsOf 1794 images, GPT4V returned valid results for 1772 (98%). It had an accuracy of 0.84 for two-class classification (BBPS 0–1 vs 2–3) and 0.74 for four-class classification (BBPS 0, 1, 2, 3). Macro-averaged F1 scores were 0.81 and 0.63, respectively. Qualitatively, most errors arose from misclassification of BBPS 1 as 2. These results compare favourably with current methods using large amounts of training data, which achieve an accuracy in the range of 0.8–0.9.ConclusionThis study provides proof-of-concept that a vision-language LLM is able to perform BBPS classification accurately, without large training datasets. This represents a paradigm shift in AI classification methods in medicine, where many diseases lack sufficient data to train traditional AI models. An LLM with appropriate examples may be used in such cases.

Aim The main treatment for meibomian gland dysfunction (MGD), a major cause of dry eye, is eyelid warming. Lack of compliance is the main reason for treatment failure. This has led to the development of eyelid-warming devices that are safe, effective and convenient. To obtain robust evidence demonstrating their efficacy, the authors conducted a 3-arm randomized clinical study. Methods The authors conducted a 3-month assessor-blinded, randomized, controlled trial of patients from the Singapore National Eye Centre experiencing at least one of eight dry eye symptoms ‘often’ or ‘all the time’. Patients who wore contact lenses, had an active infection or known diagnosis of thyroid dysfunction and rheumatoid arthritis were excluded from the study. MGD participants were randomly assigned to warm towel ( n  = 25), EyeGiene ® (Eyedetec Medical Inc., Danville, CA, USA) ( n  = 25) and Blephasteam ® (Spectrum Thea Pharmaceuticals LTD, Macclesfield, UK) ( n  = 25) treatments. The primary efficacy and safety outcomes included the proportions of participants with improved symptoms and changes in best corrected visual acuity (BCVA), respectively. Other outcomes included tear break up time (TBUT), Schirmer test, corneal fluorescein dye staining and number of visibly occluded meibomian gland (MG) orifices. Results The study population was 53.5 ± 11.1 years old and predominantly Chinese. For severity of symptom after 3 months of treatment, 78.3% Blephasteam ® participants reported improvement compared to 45.5% warm towel participants ( p  = 0.023). The corresponding proportions for improvement in the frequency of symptoms were 82.6% and 50.0%, respectively ( p  = 0.020). The proportions of improvement of symptoms in EyeGiene ® patients were not significantly different from warm towel intervention. At 1 month of treatment, the crude odds ratio of improvement of severity of irritation for Blephasteam ® compared to control was 3.0 (95% CI 0.88–10.18). However, the odds ratio adjusted by age was 5.67 (1.30–24.66). The lid-warming treatments did not significantly change the TBUT, Schirmer test results or number of visibly occluded MGs in the study period. All treatment modalities did not worsen BCVA after 3 months. Conclusion Blephasteam ® is more effective than warm towel for MGD treatment, with warm towel and EyeGiene ® being comparable effective. Older age might predict for treatment efficacy. All studied therapies were safe for visual acuity (VA) for 3 months of treatment.