The Lancet Digital Health

IntroductionThe novel coronavirus SARS-CoV2 and the associated disease, Covid-19, continue to pose a global health threat. The CovidCalculatorUK is an open-source online tool (covidcalculatoruk.org) that estimates the probability that an individual patient, who presents to a UK hospital, will later test positive for SARS-CoV2. The objective is to aid cohorting decisions and minimise nosocomial transmission of SARS-CoV2. MethodsThis n = 500 prospective, observational, multicentre, validation stu...

Lymphomas vary in terms of clinical behavior, morphology, and response to therapies and thus accurate classification is essential for appropriate management of patients. In this study, using a set of 670 cases of lymphoma obtained from a center in Guatemala City, we propose an interpretable machine learning method, LymphoML, for lymphoma subtyping into eight diagnostic categories. LymphoML sequentially applies steps of (1) object segmentation to extract nuclei, cells, and cytoplasm from hematoxy...

The use of non-invasive temperature testing methods like temporal artery thermometers (TATs) is growing exponentially in the face of the ongoing COVID-19 pandemic. We performed a retrospective analysis of over 1.8 million emergency department electronic health records to identify assess the performance of TAT measurement using patients with near-contemporaneous temperature measurements taken via rectal or oral approaches. Using over 17,000 matched measurements, we show poor fever sensitivity usi...

BackgroundPrimary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection. ObjectiveWe built an artificial intelligence model to provide real-time risk assessment about primary immunodeficiency and to facilitate prescriptive analytics for initiating the most appropriate diagnostic...

AimsDeep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. Here, we developed a DL assistant for diagnosing H. pylori in gastric biopsies and te...

ObjectivesDevelop an interpretable AI algorithm to rule out normal large bowel endoscopic biopsies saving pathologist resources. DesignRetrospective study. SettingOne UK NHS site was used for model training and internal validation. External validation conducted on data from two other NHS sites and one site in Portugal. Participants6,591 whole-slides images of endoscopic large bowel biopsies from 3,291 patients (54% Female, 46% Male). Main outcome measuresArea under the receiver operating cha...

BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illness...

The Updated Sydney System (USS) provides a standardized framework for grading gastritis and stratifying gastric cancer risk. However, subjective observer variability and labor-intensive workflows impede its routine clinical use. To address these challenges, we developed SydneyMTL, a multi-task deep learning framework that uses Multiple Instance Learning (MIL) with task-specific attention pooling to predict severity grades across all five USS attributes simultaneously. Trained on an unprecedented...

Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate the diagnostic decision-making and reduce the workload of pathologists, we present an AI-based pre-screening tool capable of identifying normal and neoplastic colon biopsies. To learn the differential histological patterns from whole slides images (WSIs) stained with hematoxylin and eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level...

The tumor-stroma ratio (TSR) is an established prognostic biomarker across several cancer types, yet its manual assessment remains labour-intensive and subject to inter-observer variability. An artificial intelligence (AI)-based estimate could offer an efficient, consistent alternative for this task. In this study, quantitative comparisons were made between expert humans and an AI model for TSR estimation. Using two independent, multi-institutional histopathology datasets, an Attention U-Net was...

PurposeAccurate cancer subtyping is crucial for effective treatment; however, it presents challenges due to overlapping morphology and variability among pathologists. Although deep learning (DL) methods have shown potential, their application to gigapixel whole slide images (WSIs) is often hindered by high computational demands and the need for efficient, context-aware feature aggregation. This study introduces LiteMIL, a computationally efficient transformer-based multiple instance learning (MI...

BackgroundSepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths annually. Natural language processing (NLP)-driven clinical decision support systems (CDSS) have been deployed in several NHS Trusts to enable automated early detection of sepsis from unstructured clinical notes, yet causal evidence of their effectiveness at the hospital level remains limited. ObjectiveTo estimate the causal effect of implementing N...

The integration of AI in histopathology represents a significant advancement in diagnosing metastatic cancers. LYDIA (LYmph noDe assIstAnt) is a commercially available AI-powered tool designed to annotate tumors in lymph node sections to aid histopathologists in diagnosing metastases. This study rigorously evaluates LYDIAs standalone performance and the time and cost benefits on diagnostic workflow. In this study, LYDIAs performance was rigorously evaluated on a blind image dataset comprising 36...

For breast cancer, the Ki-67 index gives important information on the patients prognosis and may predict the response to therapy. However, semi-automated methods for Ki-67 index calculation are prone to intra-and inter-observer variability, while fully automated machine learning models based on nuclei segmentation, classification and counting require training on large datasets with precise annotations down to the level of individual nuclei, which are hard to obtain. We design a neural network th...

Image classification on digital pathology images relies heavily on convolutional neural networks (CNNs), yet the behavior of alternative neural computing paragigms in this domain remains insufficiently characterized. Spiking neural networks (SNNs), which process information through event-driven spike-based dynamics, have recently become trainable at scale but have not been evaluated under standardized colorectal pathology benchmarks. This study presents the first controlled comparison of SNNs an...

The endoscopic submucosal dissection (ESD) is the preferred technique for treating early gastric cancers including poorly differentiated adenocarcinoma without ulcerative findings. The histopathological classification of poorly differentiated adenocarcinoma including signet ring cell carcinoma is of pivotal importance for determining further optimum cancer treatment(s) and clinical outcomes. Because conventional diagnosis by pathologists using microscopes is time-consuming and limited in terms o...

Pathology reporting of colorectal cancer (CRC) follows the International Collaboration on Cancer Reporting (ICCR) guidelines which define a set of 25 diagnostic report elements. To further develop the CRC diagnostic routine, multiple computational tools have been proposed in the last years. Despite the excellent sensitivity and potential advantages, many tools do not reach clinical deployment, suggesting that there are critical challenges to address when developing these algorithms. To summarize...

AO_SCPLOWBSTRACTC_SCPLOWWith the rise of digital pathology, integrating digital slides with deep learning-based decision support systems is becoming increasingly common in clinical practice. Tissue region segmentation which is distinguishing tissue from background/artefacts, is an important pre-requisite in many digital pathology pipelines both for the laboratories as their first step in digitalizing the glass slides of tissue samples and turning them to whole slide images (WSIs) using scanners,...

Intraoperative pathology remains constrained by ice crystal artifacts in frozen sections and the high cost of emerging slide-free optical methods. Here, we introduce FLASH-Path, a rapid slide-free technique enabling subcellular-resolution imaging of centimeter-scale tissues in 10 minutes. By replacing mechanical thin sectioning or optical thin-layer excitation with thin-layer ([≤]10 {micro}m) fluorescent labeling using commercially available probes, FLASH-Path achieves artifact-free visualiza...

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multiparameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colore...