An open source pipeline for quantitative immunohistochemistry image analysis of inflammatory skin disease using artificial intelligence

With its development, artificial intelligence has formed the basis for many studies aimed at facilitating people's lives. More successful results have been tried to be obtained with the increasing data and developing equipment in these studies. It is seen that these developments in artificial intelligence are reflected in the studies related to sign language conversion. In this study, a data set belonging to the letters in the Turkish Sign Language Alphabet was created, and the classification process was carried out with both the deep learning model we created and VGG16, Inceptionv3, Resnet, and Mobilnet models, which are frequently used in image classification. In addition, an open-source data set containing the letters in the American Sign Language Alphabet was organized similar to the data set containing the letters in the Turkish Sign Language Alphabet we created, and Deep Learning models were used to classify the letters in the American Sign Language Alphabet by using this data set. Performance evaluations of the classifications made by Deep Learning Models using both data sets were made. With this study, the results obtained from training Deep Learning methods with different data sets were compared. In addition, it is thought that the study will be useful in determining both the data set and the deep learning method to be used for the studies on the recognition of Sign Language Letters.

Purpose This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields. Design/methodology/approach The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks. Findings China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns. Practical implications The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields. Social implications The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits. Originality/value This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.

Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma☆

BackgroundWith the rapid advances in artificial intelligence—particularly convolutional neural networks—researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. End-to-end deep-learning models simultaneously perform feature extraction and classification, capturing not only traditional radiomic signatures such as tumour density and texture but also peri-tumoural micro-environmental cues, thereby offering a higher theoretical performance ceiling than hand-crafted radiomics coupled with classical machine learning. Nevertheless, the need for large, well-annotated datasets, the domain shifts introduced by heterogeneous scanning protocols and preprocessing pipelines, and the “black-box” nature of neural networks all hinder clinical adoption. To address fragmented evidence and scarce external validation, we conducted a systematic review to appraise the true performance of deep-learning and radiomics models for EGFR prediction and to identify barriers to clinical translation, thereby establishing a baseline for forthcoming multicentre prospective studies.MethodsFollowing PRISMA 2020, we searched PubMed, Web of Science and IEEE Xplore for studies published between 2018 and 2024. Fifty-nine original articles met the inclusion criteria. QUADAS-2 was applied to the eight studies that developed models using real-world clinical data, and details of external validation strategies and performance metrics were extracted systematically.ResultsThe pooled internal area under the curve (AUC) was 0.78 for radiomics–machine-learning models and 0.84 for deep-learning models. Only 17 studies (29%) reported independent external validation, where the mean AUC fell to 0.77, indicating a marked domain-shift effect. QUADAS-2 showed that 31% of studies had high risk of bias in at least one domain, most frequently in Index Test and Patient Selection.ConclusionAlthough deep-learning models achieved the best internal performance, their reliance on single-centre data, the paucity of external validation and limited code availability preclude their use as stand-alone clinical decision tools. Future work should involve multicentre prospective designs, federated learning, decision-curve analysis and open sharing of models and data to verify generalisability and facilitate clinical integration.

Early diagnosis of mild cognitive impairment and Alzheimer's disease using multimodal feature-based deep learning models in a Chinese elderly population.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

This study investigates how generative artificial intelligence (AI) could influence the media environment of that country. Given their prevalence in the industry, it seeks to uncover how general process of national advancement is supported by AI-powered technologies in media content generation, distribution, and personalisation. The research gathers data from media professionals, techies, and communication academics in major Chinese cities all around using properly crafted questionnaires in a quantitative manner. One examined the interactions of AI-driven media innovation, modernising pace, and consequences of national development using statistical methods like structural equation modelling and regression analysis. Linking modernism with national development projects, the indices of generative AI used in media show a definite positive link with their influence. Generative AI is becoming more and more important for China's attempts to modernise its communication infrastructure and undergo digital transformation, thereby enhancing the nation's cultural impact, technical capacity, and media creation. Modernism approaches enhance the possibilities presented by AI in media; this research reveals more dynamic storylines, audience interaction feasible, and global outreach. Every one of these components helps to forward objectives for national growth. Regarding developing plans to mix AI with goals for sustainable development, the findings give legislators, media outlets, and AI developers’ useful insights. This study offers a structure for further studies in new media technologies and national transformation by means of an experimental interaction between generative AI and national development seen from the perspective of modernism. It opens the conversation on the social opportunities AI offers.

Vocal disorders present significant challenges for patients and clinicians, impacting communication and quality of life. The development of artificial intelligence (AI) technologies offers promising possibilities for improving the assessment and management of vocal disorders. This study aims to evaluate the effectiveness and applicability of different AI approaches in this field through a comparative study of AI-enabled medical assessment and assistance for vocal disorders. Various AI techniques, including machine learning algorithms, deep learning models, and natural language processing methods, are explored in the context of diagnosing vocal disorders, planning treatments, and managing patients. The insights gained from this comparative study contribute to understanding the role of AI in transforming healthcare delivery for vocal disorders, highlighting opportunities, challenges, and future directions for utilizing AI to enhance medical assessment and assistance in this specialized field.

Since 2019, artificial intelligence (AI) has played an increasingly central role in daily life. Yet, this phase in AI’s evolution is only the beginning of even greater advancements. Catholic institutions, cognizant of the crucial role the Church must assume in the development of AI, have increasingly engaged with this technological frontier. Among these institutions is Longbeard, which has undertaken the creation of Magisterium AI, a compound AI system that leverages several AI technologies and a large database of Catholic knowledge to produce faithful answers. This article offers a critical analysis of the current state of Magisterium AI, drawing on its 2025 Report, Investor Deck, user feedback, and direct engagement with the system to evaluate its alignment with Catholic ethical principles. Specifically, it highlights the expansion of the database as a key area for improvement, while reflecting on the complexities inherent in such an undertaking. The article concludes that the development of a Catholic AI entails more than simply amassing documents in a database. It requires endowing the AI system with a profound grasp of Scripture and its interpretive traditions; a comprehensive knowledge of theology as a whole; a broad acquaintance with the history of ideas, including the development of culture, art, philosophy, and theology; and a finely honed awareness of the evolving nuances of language and cultural sensibilities through which eternal truths are expressed.

Today, pathology imaging is one of the most common and inexpensive diagnostic/prognostic tools used in oncology, while more sophisticated methods such as next generation sequencing (NGS) remain relatively expensive and not routinely used in a clinical setting. Deep convolutional neural networks (CNNs) have emerged as an important image analysis technology enhancing the workflow of pathologists and improving the prediction of patient prognosis and response to treatment​​. Recently, a few attempts have been made to predict molecular features from tissue imaging using CNNs. While these preliminary results are encouraging, there have been no systematic attempts to link Whole Slide Images (WSIs) to transcriptomic profiles. In this study, we developed a cutting-edge deep learning model named HE2RNA, specifically customized for the direct prediction of gene expression from H&E-stained WSIs without need for annotation from pathologists. Our model was trained and tested on 8,725 patients from 28 different cancer types available at The Cancer Genome Atlas (TCGA). HE2RNA accurately predicted the expression of six gene signatures related to well known cancer hallmarks (angiogenesis, hypoxia, DNA repair, cell cycle and immunity) and performed particularly well for signalling pathways involved in immune cell activation. This indicates that suitably designed deep learning models can recognize subtle structures in tissue imaging and relate them to molecular portraits. Moreover, HE2RNA is designed to generate a spatial representation (virtual map) of any well-predicted gene expression overlaying the H&E slide. Such a virtual map was validated on a double-stained H&E/CD3 slide obtained from an independent hepatocellular carcinoma sample. This spatialization could be useful in augmenting the pathologists' workflow by providing a virtual multiplexed staining for each H&E slide while overcoming the technical issues associated with immunohistochemistry. Various important prognostic factors, such as microsatellite instability (MSI), are derived from molecular features. Microsatellite instability refers to the hypermutability of short repetitive genomic sequences caused by impaired DNA mismatch repair. These mutations frequently observed in gastric and colorectal cancer are associated with better response to immunotherapy. We show that the transcriptomic representation learned by our model can be used to improve the performance of MSI status prediction for small datasets of WSI. This type of setting is common since large databases of matched RNA-Seq profiles and WSI are widely available, while databases of matched MSI status and WSI are more scarce. In the future, such technologies could therefore facilitate universal screening of molecular biomarkers and improved identification of patients that could benefit from new therapeutic strategies. Citation Format: Elodie Pronier, Benoît Schmauch, Alberto Romagnoni, Charlie Saillard, Pascale Maillé, Julien Calderaro, Meriem Sefta, Sylvain Toldo, Mikhail Zaslavskiy, Thomas Clozel, Matahi Moarii, Pierre Courtiol, Gilles Wainrib. HE2RNA: A deep learning model for transcriptomic learning from digital pathology [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2105.

Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study

A comprehensive deep learning model for motor phenotypes of Parkinson's disease using three-dimensional kinect V2 detectors.

Groundwater depth has complex non-linear relationships with climate, groundwater extraction, and surface water flows. To understand the importance of each predictor and predictand (groundwater depth), different artificial intelligence (AI) techniques have been used. In this research, we have proposed a Deep Learning (DL) model to predict groundwater depths. The DL model is an extension of the conventional neural network with multiple layers having non-linear activation function. The feasibility of the DL model is assessed with well-established framework models [Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR)]. The area selected for this study is Konan basin located in the Kochi Prefecture of Japan. The hydro-meteorological and groundwater data used are precipitation, river stage, temperature, recharge and groundwater depth. Identical set of inputs and outputs of all the selected stations were used to train and validate the models. The predictive accuracy of the DL, ELM and GPR models has been assessed considering suitable goodness-of-fit criteria. During training period, the DL model has a very good agreement with the observed data (RMSE = 0.04, r = 0.99 and NSE = 0.98) and during validation period, its performance is satisfactory (RMSE = 0.08, r = 0.95 and NSE = 0.87). To check practicality and generalization ability of the DL model, it was re-validated at three different stations (E2, E3 and E6) of the same unconfined aquifer. The significant prediction capability and generalization ability makes the proposed DL model more reliable and robust. Based on the finding of this research, the DL model is an intelligent tool for predicting groundwater depths. Such advanced AI technique can save resources and labor conventionally employed to estimate various features of complex groundwater systems.

Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene (c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c-KIT mutations in HE slides.

Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.