[Auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection for pulmonary and extra-pulmonary tuberculosis among the elderly].

OBJECTIVE: To evaluate the auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection (T-SPOT.TB) for pulmonary and extra-pulmonary tuberculosis among the elderly. METHODS: A total of 173 elderly patients at ages of 60 years and older and with suspected tuberculosis that were admitted to People's Hospital of Xinjiang Uygur Autonomous Region during the period from October 2022 through February 2024 were enrolled, and all patients underwent T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests. The etiological tests of MTB served as a gold standard, and the diagnostic values of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests for pulmonary and extra-pulmonary tuberculosis were compared among the elderly patients. RESULTS: Of the 173 elderly patients suspected of tuberculosis, there were 44 patients definitely diagnosed with pulmonary tuberculosis, 30 cases with extra-pulmonary tuberculosis, and 99 cases without tuberculosis. The sensitivities of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests were 86.5%, 27.0% and 54.1% for diagnosis of tuberculosis. The sensitivities of T-SPOT.TB were 86.4% and 86.7% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with an 80.8% specificity for diagnosis of tuberculosis. The sensitivities of GeneXpert MTB/RIF were 56.8% and 50.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each, and the sensitivities of acid fast staining were 31.8% and 20.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each. In addition, the areas under the receiver operating characteristic curve were 0.836, 0.635 and 0.770 for diagnosis of tuberculosis with T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests among the elderly patients, respectively. CONCLUSIONS: T-SPOT.TB has a high auxiliary diagnostic value for both pulmonary and extra-pulmonary tuberculosis among elderly patients.

[Auxiliary diagnosis of visceral leishmaniasis with metagenomic next-generation sequencing: a case report].

This case report summarizes the experience from diagnosis and treatment of a patient with repeated high fever, hepatosplenomegaly and pancytopenia. Following exclusion of bacterial, viral, fungal infections and hematological diseases, metagenomic next-generation sequencing of the patient's peripheral blood revealed Leishmania infantum infection, and rK39 rapid diagnostic test showed positive for anti-Leishmania antibody, while microscopic examination of bone marrow smears identified Leishmania amastigotes. Therefore, the case was definitively diagnosed as visceral leishmaniasis, and given anti-infective treatment with sodium antimony gluconate and hormone, hepatoprotection, elevation of white blood cell counts and personalized nursing. Then, the case was cured and discharged from hospital. Metagenomic next-generation sequencing is of great value in etiological detection of fever patients with unknown causes, which deserves widespread clinical applications.

Artificial intelligence assisted diagnosis of early tc markers and its application.

Thyroid cancer (TC) is a common endocrine malignancy with an increasing incidence worldwide. Early diagnosis is particularly important for TC patients, because it allows patients to receive treatment as early as possible. Artificial intelligence (AI) provides great advantages for complex healthcare systems by analyzing big data based on machine learning. Nowadays, AI is widely used in the early diagnosis of cancer such as TC. Ultrasound detection and fine needle aspiration biopsy are the main methods for early diagnosis of TC. AI has been widely used in the detection of malignancy in thyroid nodules by ultrasound images, cytopathology images and molecular markers. It shows great potential in auxiliary medical diagnosis. The latest clinical trial has shown that the performance of AI models matches with the diagnostic efficiency of experienced clinicians, and more efficient AI tools will be developed in the future. Therefore, in this review, we summarized the recent advances in the application of AI algorithms in assessing the risk of malignancy in thyroid nodules. The objective of this review was to provide a data base for the clinical use of AI-assisted diagnosis in TC, as well as to provide new ideas for the next generation of AI-assisted diagnosis in TC.

Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation.

BACKGROUND: Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. OBJECTIVE: This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). METHODS: The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. RESULTS: The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. CONCLUSIONS: The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis.

DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes.

X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns.

DIEANet: an attention model for histopathological image grading of lung adenocarcinoma based on dimensional information embedding.

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection.

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries.

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.

An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.

OBJECTIVE: To evaluate the performance of two lightweight neural network models in the diagnosis of common fundus diseases and make comparison to another two classical models. METHODS: A total of 16,000 color fundus photography were collected, including 2000 each of glaucoma, diabetic retinopathy (DR), high myopia, central retinal vein occlusion (CRVO), age-related macular degeneration (AMD), optic neuropathy, and central serous chorioretinopathy (CSC), in addition to 2000 normal fundus. Fundus photography was obtained from patients or physical examiners who visited the Ophthalmology Department of Beijing Tongren Hospital, Capital Medical University. Each fundus photography has been diagnosed and labeled by two professional ophthalmologists. Two classical classification models (ResNet152 and DenseNet121), and two lightweight classification models (MobileNetV3 and ShufflenetV2), were trained. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the performance of the four models. RESULTS: Compared with the classical classification model, the total size and number of parameters of the two lightweight classification models were significantly reduced, and the classification speed was sharply improved. Compared with the DenseNet121 model, the ShufflenetV2 model took 50.7% less time to make a diagnosis on a fundus photography. The classical models performed better than lightweight classification models, and Densenet121 showed highest AUC in five out of the seven common fundus diseases. However, the performance of lightweight classification models is satisfying. The AUCs using MobileNetV3 model to diagnose AMD, diabetic retinopathy, glaucoma, CRVO, high myopia, optic atrophy, and CSC were 0.805, 0.892, 0.866, 0.812, 0.887, 0.868, and 0.803, respectively. For ShufflenetV2model, the AUCs for the above seven diseases were 0.856, 0.893, 0.855, 0.884, 0.891, 0.867, and 0.844, respectively. CONCLUSION: The training of light-weight neural network models based on color fundus photography for the diagnosis of common fundus diseases is not only fast but also has a significant reduction in storage size and parameter number compared with the classical classification model, and can achieve satisfactory accuracy.

Application status of artificial intelligence techniques in retinoblastoma / 国际眼科杂志(Guoji Yanke Zazhi)

Retinoblastoma is a kind of malignant eye tumor commonly seen in children, which is one of the main causes threatening children's vision and life. The diagnosis and evaluation of retinoblastoma has always been a hot topic in clinic. In the past few years, the application of artificial intelligence(AI)technology has made significant progress in the medical field, providing new opportunities and challenges for the diagnosis and treatment of retinoblastoma, for example, the use of AI algorithms to analyze massive clinical data, which can help doctors diagnose the disease more accurately and provide personalized treatment plans. In addition, AI technology also plays an important role in medical image analysis, genomics research and other aspects, which can help the development of new drugs and improve patient prognosis. This article reviews the application progress of AI in retinoblastoma.

Self-supervised tumor segmentation and prognosis prediction in osteosarcoma using multiparametric MRI and clinical characteristics.

BACKGROUND AND OBJECTIVE: Osteosarcoma has a high mortality among malignant bone tumors. MRI-based tumor segmentation and prognosis prediction are helpful to assist doctors in detecting osteosarcoma, evaluating the patient's status, and improving patient survival. Current intelligent diagnostic approaches focus on segmentation with single-parameter MRI, which ignores the nature of MRI resulting in poor performance, and lacks the connection with prognosis prediction. Besides, osteosarcoma is a rare disease, and their few labeled data may lead to model overfitting. METHODS: We propose a three-stage pipeline for segmentation and prognosis prediction of osteosarcoma to assist doctors in diagnosis. First, we propose the Multiparameter Fusion Contrast Learning (MPFCLR) algorithm to share pre-training weights for the segmentation model using unlabeled data. Then, we construct a multiparametric fusion network (MPFNet), which fuses the complementary features from multiparametric MRI (CE-T1WI, T2WI). It can automatically segment tumor and necrotic regions. Finally, a fusion nomogram is constructed by segmentation masks and clinical characteristics (volume, tumor spread) to predict the patient's prognostic status. RESULTS: Our experiments used data from 136 patients at the Second Xiangya Hospital in China. According to experiments, the MPFNet achieves 84.19 % mean DSC and 84.56 % mean F1-score in segmenting tumor and necrotic regions, surpassing existing models and single-parameter MRI input for osteosarcoma segmentation. Besides, MPFCLR improves the segmentation performance and convergence speed. In prognosis prediction, our fusion nomogram (C-index: 0.806, 95 %CI: 0.758-0.854) is better than radiomics (C-index: 0.753, 95 %CI: 0.685-0.841) and clinical (C-index: 0.794, 95 %CI: 0.735-0.854) nomograms in predictive performance. Compared to the comparison models, our model is closest to the prediction model based on physician annotations. Moreover, it can accurately distinguish the patients' prognostic status with good or poor. CONCLUSION: Our proposed solution can provide references for clinicians to detect osteosarcoma, evaluate patient status, and make personalized decisions. It can reduce delayed treatment or overtreatment and improve patient survival.

Ultrasound-based detection of inflammatory changes for early diagnosis and risk model construction of psoriatic arthritis.

OBJECTIVES: Psoriatic arthritis (PsA) is the most prevalent coexisting condition associated with psoriasis. Early-stage PsA patients always present unspecific and subtle clinical manifestations causing delayed diagnosis and leading to unfavorable health outcomes. The application of ultrasound enables precise identification of inflammatory changes in musculoskeletal structures. Hence, we constructed ultrasound models to aid early diagnosis of PsA. METHODS: This is a cross-sectional study carried out in the Department of Dermatology at West China Hospital (October 2018-April 2021). All participants underwent thorough ultrasound examinations. Participants were classified into the under 45 group (18 ≤ age ≤ 45) and over 45 group (age > 45) and then randomly grouped into derivation and test cohort (7:3). Univariable logistic regression, least absolute shrinkage and selection operator, and multivariable logistic regression visualized by nomogram were conducted in order. Receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were performed for model verification. RESULTS: A total of 1256 participants were included, with 767 participants in the under 45 group and 489 in the over 45 group. Eleven and sixteen independent ultrasonic variables were finally selected to construct the under 45 and over 45 model with the area under the ROC of 0.83 (95%CI: 0.78-0.87) and 0.83 (95%CI: 0.78-0.88) in derivation cohort, respectively. The DCA and CICA analyses showed good clinical utility of the two models. CONCLUSION: The implementation of the ultrasound models could streamline the diagnostic process for PsA in psoriasis patients, leading to expedited evaluations while maintaining diagnostic accuracy.

Lesion-aware attention network for diabetic nephropathy diagnosis with optical coherence tomography images.

Purpose: For early screening of diabetic nephropathy patients, we propose a deep learning algorithm to screen high-risk patients with diabetic nephropathy from retinal images of diabetic patients. Methods: We propose the use of attentional mechanisms to improve the model's focus on lesion-prone regions of retinal OCT images. First, the data is trained using the base network and the Grad-CAM algorithm locates image regions that have a large impact on the model output and generates a rough mask localization map. The mask is used as a auxiliary region to realize the auxiliary attention module. We then inserted the region-guided attention module into the baseline model and trained the CNN model to guide the model to better focus on relevant lesion features. The proposed model improves the recognition of the lesion region. Results: To evaluate the lesion-aware attention network, we trained and tested it using OCT volumetric data collected from 66 patients with diabetic retinal microangiopathy (89 eyes, male = 43, female = 23). There were 45 patients (60 eyes, male=27, female = 18) in DR group and 21 patients (29 eyes, male = 16, female = 5) in DN group. Our proposed model performs even better in disease classification, specifically, the accuracy of the proposed model was 91.68%, the sensitivity was 89.99%, and the specificity was 92.18%. Conclusion: The proposed lesion-aware attention model can provide reliable screening of high-risk patients with diabetic nephropathy.

[A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction].

Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.

Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning.

BACKGROUND: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. METHODS: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. RESULTS: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. CONCLUSION: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.

MWG-UNet: Hybrid Deep Learning Framework for Lung Fields and Heart Segmentation in Chest X-ray Images.

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%.

Deep Learner System Based on Focal Color Retinal Fundus Images to Assist in Diagnosis.

Retinal diseases are a serious and widespread ophthalmic disease that seriously affects patients' vision and quality of life. With the aging of the population and the change in lifestyle, the incidence rate of retinal diseases has increased year by year. However, traditional diagnostic methods often require experienced doctors to analyze and judge fundus images, which carries the risk of subjectivity and misdiagnosis. This paper will analyze an intelligent medical system based on focal retinal image-aided diagnosis and use a convolutional neural network (CNN) to recognize, classify, and detect hard exudates (HEs) in fundus images (FIs). The research results indicate that under the same other conditions, the accuracy, recall, and precision of the system in diagnosing five types of patients with pathological changes under color retinal FIs range from 86.4% to 98.6%. Under conventional retinopathy FIs, the accuracy, recall, and accuracy of the system in diagnosing five types of patients ranged from 70.1% to 85%. The results show that the application of focus color retinal FIs in the intelligent medical system has high accuracy and reliability for the early detection and diagnosis of diabetic retinopathy and has important clinical applications.

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Necrotizing enterocolitis (NEC), with the main manifestations of bloody stool, abdominal distension, and vomiting, is one of the leading causes of death in neonates, and early identification and diagnosis are crucial for the prognosis of NEC. The emergence and development of machine learning has provided the potential for early, rapid, and accurate identification of this disease. This article summarizes the algorithms of machine learning recently used in NEC, analyzes the high-risk predictive factors revealed by these algorithms, evaluates the ability and characteristics of machine learning in the etiology, definition, and diagnosis of NEC, and discusses the challenges and prospects for the future application of machine learning in NEC.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with echogenic foci and to investigate how the AI-assisted DL model can enhance radiologists' diagnostic performance. METHODS: For this retrospective study, a total of 2724 ultrasound (US) scans were collected from two independent institutions, encompassing 1038 echogenic foci nodules. All echogenic foci were confirmed by pathology. Three DL segmentation models (DeepLabV3+, U-Net, and PSPNet) were developed, with each model using two different backbones to extract features from the nodular regions with echogenic foci. Evaluation indexes such as Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA), and Dice coefficients were employed to assess the performance of the segmentation model. The model demonstrating the best performance was selected to develop the AI-assisted diagnostic software, enabling radiologists to benefit from AI-assisted diagnosis. The diagnostic performance of radiologists with varying levels of seniority and beginner radiologists in assessing high-echo nodules was then compared, both with and without the use of auxiliary strategies. The area under the receiver operating characteristic curve (AUROC) was used as the primary evaluation index, both with and without the use of auxiliary strategies. RESULTS: In the analysis of Institution 2, the DeepLabV3+ (backbone is MobileNetV2 exhibited optimal segmentation performance, with MIoU = 0.891, MPA = 0.945, and Dice = 0.919. The combined AUROC (0.693 [95% CI 0.595-0.791]) of radiology beginners using AI-assisted strategies was significantly higher than those without such strategies (0.551 [0.445-0.657]). Additionally, the combined AUROC of junior physicians employing adjuvant strategies improved from 0.674 [0.574-0.774] to 0.757 [0.666-0.848]. Similarly, the combined AUROC of senior physicians increased slightly, rising from 0.745 [0.652-0.838] to 0.813 [0.730-0.896]. With the implementation of AI-assisted strategies, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both senior physicians and beginners in the radiology department underwent varying degrees of improvement. CONCLUSIONS: This study demonstrates that the DL-based auxiliary diagnosis model using US static images can improve the performance of radiologists and radiology students in identifying thyroid echogenic foci.

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.

BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. METHODS: This investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500-675 nm band range. RESULTS: The classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. CONCLUSIONS: Our findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.