Effect of novel lyophilized extenders on stallion post-thaw sperm motility.

The aim of this study was to assess the effect of lyophilized freezing extenders, which can be stored at room temperature, on stallion post-thaw sperm total motility (TM). Ejaculates of 28 stallions were frozen with four different extenders: two commercial freezing extenders offered worldwide and two novel lyophilized extenders (STAR and MX3), and two different cryopreservation protocols (CP1 with an equilibration period of 20 min. and CP2 with an equilibration period of 60 min.). The TM was assessed after thaw. Mean TM did not show significant differences between cryopreservation protocols within each extender. Mean TM was greater in samples diluted with STAR than in samples diluted with Botucrio (P ˂ 0.05), but no significant differences were observed for this variable between the other studied extenders. From all evaluated samples, twenty ejaculates showed the greatest TM when using the lyophilized extenders and the CP1. Thus, lyophilized extenders are a promising option for stallion sperm cryopreservation and have the advantage of storage and distribution at room temperature for at least one year.

BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations.

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: (1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and (2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student-teacher learning, where the training set contains fully and weakly-annotated mammograms. We provide extensive detection results on two real-world screening mammogram datasets containing incomplete annotations and show that our proposed approach achieves state-of-the-art results in the detection of malignant breast lesions with incomplete annotations.

Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images.

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.

Diabetic foot ulcers segmentation challenge report: Benchmark and analysis.

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.

The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.

Molecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays a major role in this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography with computed tomography (PET/CT) also playing a major role of rapidly increasing importance. Another key technology finding growing application across medicine and specifically in molecular imaging is the use of machine learning (ML) and artificial intelligence (AI). Several authoritative reviews are available of the role of MR-based molecular imaging with a sparsity of reviews of the role of PET/CT. This review will focus on the use of AI for molecular imaging for PCa. It will aim to achieve two goals: firstly, to give the reader an introduction to the AI technologies available, and secondly, to provide an overview of AI applied to PET/CT in PCa. The clinical applications include diagnosis, staging, target volume definition for treatment planning, outcome prediction and outcome monitoring. ML and AL techniques discussed include radiomics, convolutional neural networks (CNN), generative adversarial networks (GAN) and training methods: supervised, unsupervised and semi-supervised learning.

An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning.

The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, while interpretable models do not have competitive classification accuracy. In this paper, we introduce a new deep-learning diagnosis framework, called InterNRL, that is designed to be highly accurate and interpretable. InterNRL consists of a student-teacher framework, where the student model is an interpretable prototype-based classifier (ProtoPNet) and the teacher is an accurate global image classifier (GlobalNet). The two classifiers are mutually optimised with a novel reciprocal learning paradigm in which the student ProtoPNet learns from optimal pseudo labels produced by the teacher GlobalNet, while GlobalNet learns from ProtoPNet's classification performance and pseudo labels. This reciprocal learning paradigm enables InterNRL to be flexibly optimised under both fully- and semi-supervised learning scenarios, reaching state-of-the-art classification performance in both scenarios for the tasks of breast cancer and retinal disease diagnosis. Moreover, relying on weakly-labelled training images, InterNRL also achieves superior breast cancer localisation and brain tumour segmentation results than other competing methods.

Characterization of lyophilized equine colostrum.

Foals require maternal colostrum in the first hours of life to prevent failure of transfer of passive immunity (FTIP). Innovative storage methods such as lyophilization may enable conservation of colostrum immunoglobulins by a differentiated process of dehydration. The current study aimed to compare the quality of equine colostrum after freezing and after the lyophilization process. Thirty-one pregnant Quarter Horse mares were used. The IgG concentration of frozen and lyophilized colostrum was determined by simple radial immunodiffusion (SRID) and Brix refractometry. The physical-chemical composition (pH, total protein (TP), fat, lactose, salts, total solids (TS), and density) of the samples was evaluated and the lyophilized colostrum reconstitution test was performed. There were no significant differences (P > 0.05) in the variables IgG, fat, lactose, salts, TS, density, and pH between samples measured before and after lyophilization. There was a significant difference (P < 0.05) between the Brix average and the TP of the frozen and lyophilized colostrum samples. Lyophilization resulted in a small reduction (6.55%) in the IgG concentration measured by SRID. A strong positive correlation was observed between colostrum density and IgG concentration by SRID (r = 0.76) and between Brix and IgG concentration by SRID (r = 0.77). In the reconstitution test, the lyophilized colostrum was easily rehydrated in water, with full dilution, and remained stable. Lyophilization could be an alternative for the conservation of mare colostrum, since it is a very efficient process for retaining the physicochemical characteristics of the product, with minimal loss, particularly of IgG.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. METHODS: In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. RESULTS: A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. CONCLUSION: A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.

Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the risk and expense of surgery. This review updates the exponentially increasing literature exploring the diagnostic value of endometriosis specialist transvaginal ultrasound (eTVUS), combinations of eTVUS and specialist magnetic resonance imaging, and artificial intelligence. Concentrating on literature that emerged after the publication of the IDEA consensus in 2016, we identified 6192 publications and reviewed 49 studies focused on diagnosing endometriosis using emerging imaging techniques. The diagnostic performance of eTVUS continues to improve but there are still limitations. eTVUS reliably detects ovarian endometriomas, shows high specificity for deep endometriosis and should be considered diagnostic. However, a negative scan cannot preclude endometriosis as eTVUS shows moderate sensitivity scores for deep endometriosis, with the sonographic evaluation of superficial endometriosis still in its infancy. The fast-growing area of artificial intelligence in endometriosis detection is still evolving, but shows great promise, particularly in the area of combined multimodal techniques. We finalize our commentary by exploring the implications of practice change for surgeons, sonographers, radiologists, and fertility specialists. Direct benefits for endometriosis patients include reduced diagnostic delay, better access to targeted therapeutics, higher quality operative procedures, and improved fertility treatment plans.

Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography.

Endometriosis affects 1 in 9 women, taking 6.4 years to diagnose using conventional laparoscopy. Non-invasive imaging enables timelier diagnosis, reducing diagnostic delay, risk and expense of surgery. This review updates literature exploring the diagnostic value of specialist endometriosis magnetic resonance imaging (eMRI), nuclear medicine (NM) and computed tomography (CT). Searching after the 2016 IDEA consensus, 6192 publications were identified, with 27 studies focused on imaging for endometriosis. eMRI was the subject of 14 papers, NM and CT, 11, and artificial intelligence (AI) utilizing eMRI, 2. eMRI papers describe diagnostic accuracy for endometriosis, methodologies, and innovations. Advantages of eMRI include its: ability to diagnose endometriosis in those unable to tolerate transvaginal endometriosis ultrasound (eTVUS); a panoramic pelvic view, easy translation to surgical fields; identification of hyperintense iron in endometriotic lesions; and ability to identify super-pelvic lesions. Sequence standardization means eMRI is less operator-dependent than eTVUS, but higher costs limit its role to a secondary diagnostic modality. eMRI for deep and ovarian endometriosis has sensitivities of 91-93.5% and specificities of 86-87.5% making it reliable for surgical mapping and diagnosis. Superficial lesions too small for detection in larger capture sequences, means a negative eMRI doesn't exclude endometriosis. Combined with thin sequence capture and improved reader expertise, eMRI is poised for rapid adoption into clinical practice. NM labeling is diagnostically limited in absence of suitable unique marker for endometrial-like tissue. CT studies expose the reproductively aged to radiation. AI diagnostic tools, combining independent eMRI and eTVUS endometriosis markers, may result in powerful capability. Broader eMRI use, will optimize standards and protocols. Reporting systems correlating to surgical anatomy will facilitate interdisciplinary preoperative dialogues. eMRI endometriosis diagnosis should reduce repeat surgeries with mental and physical health benefits for patients. There is potential for early eMRI diagnoses to prevent chronic pain syndromes and protect fertility outcomes.

The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification*†.

Endometriosis is a debilitating condition affecting 5% to 10% of the women worldwide, where early detection and treatment are the best tools to manage the condition. Early detection can be done via surgery, but multi-modal medical imaging is preferable given the simpler and faster process. However, imaging-based endometriosis diagnosis is challenging as 1) there are few capable clinicians; and 2) it is characterised by small lesions unconfined to a specific location. These two issues challenge the development of endometriosis classifiers as the training datasets tend to be small and contain difficult samples, which leads to overfitting. Hence, it is important to consider generalisation techniques to mitigate this problem, particularly self-supervised pre-training methods that have shown outstanding results in computer vision and natural language processing applications. The main goal of this paper is to study the effectiveness of modern self-supervised pre-training techniques to overcome the two issues mentioned above for the classification of endometriosis from multi-modal imaging data. We also introduce a new masking image modelling self-supervised pre-training method that works with 3D multi-modal medical imaging. Furthermore, to the best of our knowledge, this paper presents the first endometriosis classifier, fine-tuned from the pre-trained model above, which works with multi-modal (i.e., T1 and T2) magnetic resonance imaging (MRI) data. Our results show that self-supervised pre-training improves endometriosis classification by as much as 31%, when compared with classifiers trained from scratch.

Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images.

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection. In this paper, we propose a new self-supervised pre-training method for MIA UAD applications, named Pseudo Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL consists of a novel optimisation method that contrasts a normal image class from multiple pseudo classes of synthesised abnormal images, with each class enforced to form a dense cluster in the feature space. In the experiments, we show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest X-ray datasets.

ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.

Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.

Carboxymethylchitosan with medium-molecular-weight affects kinectics and acrosome of stallion sperm after freezing/thawing.

Semen cryopreservation ensures the storage of stallion genetics for an unlimited time. The improvement of extenders with new antioxidant substances can optimize the properties of post-thawed semen. The study aimed to investigate the addition effect of medium-molecular-weight carboxymethylchitosan (CQm) derivates to freezing diluent of stallion sperm after freezinf/thawing. Twice a week, five ejaculates of four stallions were obtained, totalizing 20 ejaculates. Semen was diluted in commercial freezing extender (Botucrio) supplemented with CQm: control (0), 0.75, 1.5, and 3 mg/mL. Samples were filled in straws (0.5 mL) and submitted to freezing and storage at -196°C. Thawing was performed at 37°C/30 s, and the samples of each group were analyzed for kinetics, plasma membrane integrity, acrosome membrane integrity, and mitochondrial membrane potential . The addition of 1.5 and 3 mg/mL CQm showed lower values (P < .05) of total motility (TM), progressive motility (PM), curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP) and wobble (WOB), comparing to control group. Besides, it was observed lower (P < .05) percentages of sperm with intact acrosomes in the group treated with 3 mg/mL of CQm than control group. In conclusion, high concentration of medium-molecular-weight carboxymethylchitosan to freezing diluent damages kinematic and acrosome of stallion sperm after freezing/thawing.

PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors.

We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single-task setting to the meta-learning multiple-task setting to upper-bound the error evaluated on any, even unseen, tasks and samples. We also propose a generative-based approach to estimate the posterior of task-specific model parameters more expressively compared to the usual assumption based on a multivariate normal distribution with a diagonal covariance matrix. We show that the models trained with our proposed meta-learning algorithm are well-calibrated and accurate, with state-of-the-art calibration errors while still being competitive on classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.

BowelNet: Joint Semantic-Geometric Ensemble Learning for Bowel Segmentation From Both Partially and Fully Labeled CT Images.

Accurate bowel segmentation is essential for diagnosis and treatment of bowel cancers. Unfortunately, segmenting the entire bowel in CT images is quite challenging due to unclear boundary, large shape, size, and appearance variations, as well as diverse filling status within the bowel. In this paper, we present a novel two-stage framework, named BowelNet, to handle the challenging task of bowel segmentation in CT images, with two stages of 1) jointly localizing all types of the bowel, and 2) finely segmenting each type of the bowel. Specifically, in the first stage, we learn a unified localization network from both partially- and fully-labeled CT images to robustly detect all types of the bowel. To better capture unclear bowel boundary and learn complex bowel shapes, in the second stage, we propose to jointly learn semantic information (i.e., bowel segmentation mask) and geometric representations (i.e., bowel boundary and bowel skeleton) for fine bowel segmentation in a multi-task learning scheme. Moreover, we further propose to learn a meta segmentation network via pseudo labels to improve segmentation accuracy. By evaluating on a large abdominal CT dataset, our proposed BowelNet method can achieve Dice scores of 0.764, 0.848, 0.835, 0.774, and 0.824 in segmenting the duodenum, jejunum-ileum, colon, sigmoid, and rectum, respectively. These results demonstrate the effectiveness of our proposed BowelNet framework in segmenting the entire bowel from CT images.

Effect of Supplementation with Saccharomyces cerevisiae and ß-glucans to Mares During Late Gestation on Colostrum Quality and Passive Transfer of Immunity in Foals.

The objective of this study was to determine whether supplementation with Saccharomyces cerevisiae or ß-glucan, in the maternal diet during late pregnancy affects the concentration of total IgG in the colostrum of mares and influences the concentration of IgG in its foals. A total of 21 pregnant mares were used, aged 6±2 years, 3±1 pregnancies, 450±50kg in weight, and they were distributed into three groups: the control group (n=7); the S. cerevisiae group (n=7), which received 1010CFU of S. cerevisiae orally; and the ß-glucan group (n=7), which received 0.35g of ß-glucan orally. All groups started from the 300th day of their pregnancies until delivery. Samples of colostrum and serum from the mares were collected immediately after delivery. Blood samples from their foals were collected 12h after birth. The IgG measurement was performed using radial immunodiffusion. The results underwent a variance analysis. Higher concentrations of IgG were observed in the colostrum of mares that were supplemented with ß-glucans (74.14±15.25 g/L) when compared to the control group (53.80g±10.95g/L). Serum IgG concentrations of foals born to mares supplemented with Saccharomyces cerevisiae (11.57±5.05 g/L) showed a significant difference, with a higher concentration of IgG in the serum compared to the control group. Therefore, this study provides evidence that manipulation of the mares' diets in late gestation to add ß-glucan increased the IgG concentration in their colostrum. The addition of S. cerevisiae appears to improve the concentration of IgG in their foals within 12h after birth.

Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images.

Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based registration methods have demonstrated superior per-formance compared to iterative methods in just a fraction of the time. Most of the learning-based methods have focused on mono-modal image registration. The extension to multi-modal registration depends on the use of an appropriate similarity function, such as the mutual information (MI). We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network. Our results show that a small, 2-layer network produces competitive results in both mono- and multi-modal registration, with sub-second run-times. Comparisons to both iterative and deep learning-based methods show that our MI-based method produces topologically and qualitatively superior results with an extremely low rate of non-diffeomorphic transformations. Real-time clinical application will benefit from a better visual matching of anatomical structures and less registration failures/outliers.

Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis.

Background and Aims: Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia. Methods: We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies - 2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostics odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I 2, Tau2 statistics and p-value. The funnel plots and Deek's test were used to assess publication bias. Results: Twelve studies comprising of 1,361 patients (utilizing 532,328 images on which the various AI models were trained) were used. The SROC was 0.94 (95% CI: 0.92-0.96). Pooled sensitivity, specificity and diagnostic odds ratio were 90.3% (95% CI: 87.1-92.7%), 84.4% (95% CI: 80.2-87.9%) and 48.1 (95% CI: 28.4-81.5), respectively. Subgroup analysis of AI models trained only on white light endoscopy was similar with pooled sensitivity and specificity of 91.2% (95% CI: 85.7-94.7%) and 85.1% (95% CI: 81.6%-88.1%), respectively. Conclusions: AI is highly accurate at detecting early Barrett's neoplasia and validated for patients with at least high-grade dysplasia and above. Further well-designed prospective randomized controlled studies of all histopathological subtypes of early Barrett's neoplasia are needed to confirm these findings further.