Evaluation of pan-Immuno-Inflammation value for In-hospital mortality in acute pulmonary embolism patients.

Background: Pan-immuno-inflammation value (PIV) is a new and comprehensive index that reflects both the immune response and systemic inflammation in the body. Objective: The aim of this study was to investigate the prognostic relevance of PIV in predicting in-hospital mortality in acute pulmonary embolism (PE) patients and to compare it with the well-known risk scoring system, PE severity index (PESI), which is commonly used for a short-term mortality prediction in such patients. Methods: In total, 373 acute PE patients diagnosed with contrast-enhanced computed tomography were included in the study. Detailed cardiac evaluation of each patient was performed and PESI and PIV were calculated. Results: In total, 60 patients died during their hospital stay. The multivariable logistic regression analysis revealed that baseline heart rate, N-terminal pro-B-type natriuretic peptide, lactate dehydrogenase, PIV, and PESI were independent risk factors for in-hospital mortality in acute PE patients. When comparing with PESI, PIV was non-inferior in terms of predicting the survival status in patients with acute PE. Conclusion: In our study, we found that the PIV was statistically significant in predicting in-hospital mortality in acute PE patients and was non-inferior to the PESI.

Artificial Intelligence and Infectious Disease Imaging.

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

PURPOSE OF REVIEW: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). RECENT FINDINGS: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. SUMMARY: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.

Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review.

Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the architectural, engineering, and construction (AEC) industry has demonstrated that these visualization tools can be paradigm-shifting. Extended Reality (XR), an umbrella term for VR, AR, and MR technologies, has found many diverse use cases in the AEC industry. Despite this exciting trend, there is no review study on the usage of XR technologies for the condition assessment of civil structures. Thus, the present paper aims to fill this gap by presenting a literature review encompassing the utilization of XR technologies for the condition assessment of civil structures. This study aims to provide essential information and guidelines for practitioners and researchers on using XR technologies to maintain the integrity and safety of civil structures.

Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.

Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and MRI RF fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field, Etan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1gSARmax, at the lead-tip during 1.5 T MRI was determined by EM simulations. Etan values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of Etan could effectively predict 1gSARmax at the DBS lead-tip (R = 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices.

Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

BACKGROUND AND AIMS: Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. METHODS: A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. RESULTS: There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. CONCLUSIONS: Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.

Lower Respiratory Tract Infection of the Ferret by 2009 H1N1 Pandemic Influenza A Virus Triggers Biphasic, Systemic, and Local Recruitment of Neutrophils.

UNLABELLED: Infection of the lower respiratory tract by influenza A viruses results in increases in inflammation and immune cell infiltration in the lung. The dynamic relationships among the lung microenvironments, the lung, and systemic host responses during infection remain poorly understood. Here we used extensive systematic histological analysis coupled with live imaging to gain access to these relationships in ferrets infected with the 2009 H1N1 pandemic influenza A virus (H1N1pdm virus). Neutrophil levels rose in the lungs of H1N1pdm virus-infected ferrets 6 h postinfection and became concentrated at areas of the H1N1pdm virus-infected bronchiolar epithelium by 1 day postinfection (dpi). In addition, neutrophil levels were increased throughout the alveolar spaces during the first 3 dpi and returned to baseline by 6 dpi. Histochemical staining revealed that neutrophil infiltration in the lungs occurred in two waves, at 1 and 3 dpi, and gene expression within microenvironments suggested two types of neutrophils. Specifically, CCL3 levels, but not CXCL8/interleukin 8 (IL-8) levels, were higher within discrete lung microenvironments and coincided with increased infiltration of neutrophils into the lung. We used live imaging of ferrets to monitor host responses within the lung over time with [(18)F]fluorodeoxyglucose (FDG). Sites in the H1N1pdm virus-infected ferret lung with high FDG uptake had high levels of proliferative epithelium. In summary, neutrophils invaded the H1N1pdm virus-infected ferret lung globally and focally at sites of infection. Increased neutrophil levels in microenvironments did not correlate with increased FDG uptake; hence, FDG uptake may reflect prior infection and inflammation of lungs that have experienced damage, as evidenced by bronchial regeneration of tissues in the lungs at sites with high FDG levels. IMPORTANCE: Severe influenza disease is characterized by an acute infection of the lower airways that may progress rapidly to organ failure and death. Well-developed animal models that mimic human disease are essential to understanding the complex relationships of the microenvironment, organ, and system in controlling virus replication, inflammation, and disease progression. Employing the ferret model of H1N1pdm virus infection, we used live imaging and comprehensive histological analyses to address specific hypotheses regarding spatial and temporal relationships that occur during the progression of infection and inflammation. We show the general invasion of neutrophils at the organ level (lung) but also a distinct pattern of localized accumulation within the microenvironment at the site of infection. Moreover, we show that these responses were biphasic within the lung. Finally, live imaging revealed an early and sustained host metabolic response at sites of infection that may reflect damage and repair of tissues in the lungs.

Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation.

Active tuberculosis (TB) often presents with advanced pulmonary disease, including irreversible lung damage and cavities. Cavitary pathology contributes to antibiotic failure, transmission, morbidity and mortality. Matrix metalloproteinases (MMPs), in particular MMP-1, are implicated in TB pathogenesis. We explored the mechanisms relating MMP/TIMP imbalance to cavity formation in a modified rabbit model of cavitary TB. Our model resulted in consistent progression of consolidation to human-like cavities (100% by day 28), with resultant bacillary burdens (>10(7) CFU/g) far greater than those found in matched granulomatous tissue (10(5) CFU/g). Using a novel, breath-hold computed tomography (CT) scanning and image analysis protocol, we showed that cavities developed rapidly from areas of densely consolidated tissue. Radiological change correlated with a decrease in functional lung tissue, as estimated by changes in lung density during controlled pulmonary expansion (R(2) = 0.6356, p < 0.0001). We demonstrated that the expression of interstitial collagenase (MMP-1) was specifically greater in cavitary compared to granulomatous lesions (p < 0.01), and that TIMP-3 significantly decreased at the cavity surface. Our findings demonstrated that an MMP-1/TIMP imbalance is associated with the progression of consolidated regions to cavities containing very high bacterial burdens. Our model provided mechanistic insight, correlating with human disease at the pathological, microbiological and molecular levels. It also provided a strategy to investigate therapeutics in the context of complex TB pathology. We used these findings to predict a MMP/TIMP balance in active TB and confirmed this in human plasma, revealing the potential of MMP/TIMP levels as key components of a diagnostic matrix aimed at distinguishing active from latent TB (PPV = 92.9%, 95% CI 66.1-99.8%, NPV = 85.6%; 95% CI 77.0-91.9%).

In vivo prediction of tuberculosis-associated cavity formation in rabbits.

The presence of cavitary lesions in patients with tuberculosis poses a significant clinical concern due to the risk of infectivity and the risk of antibiotic treatment failure. We describe 2 algorithms that use noninvasive positron emission tomography (PET) and computed tomography (CT) to predict the development of cavitary lesions in rabbits. Analysis of the PET region of interest predicted cavitary disease with 100% sensitivity and 76% specificity, and analysis of the CT region of interest predicted cavitary disease with 83.3% sensitivity and 76.9% specificity. Our results show that restricting our analysis to regions with high [(18)F]-fluorodeoxyglucose uptake provided the best combination of sensitivity and specificity.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.

Recombinant human factor VIIa for alveolar hemorrhage following allogeneic stem cell transplantation.

The mortality rate of alveolar hemorrhage (AH) after allogeneic hematopoietic stem cell transplantation is greater than 60% with supportive care and high-dose steroid therapy. We performed a retrospective cohort analysis to assess the benefits and risks of recombinant human factor VIIa (rFVIIa) as a therapeutic adjunct for AH. Between 2005 and 2012, 57 episodes of AH occurred in 37 patients. Fourteen episodes (in 14 patients) were treated with steroids alone, and 43 episodes (in 23 patients) were treated with steroids and rFVIIa. The median steroid dose was 1.9 mg/kg/d (interquartile range [IQR], 0.8 to 3.5 mg/kg/d; methylprednisolone equivalents) and did not differ statistically between the 2 groups. The median rFVIIa dose was 41 µg/kg (IQR, 39 to 62 µg/kg), and a median of 3 doses (IQR, 2 to 17) was administered per episode. Concurrent infection was diagnosed in 65% of the episodes. Patients had moderately severe hypoxia (median PaO2/FiO2, 193 [IQR, 141 to 262]); 72% required mechanical ventilation, and 42% survived to extubation. The addition of rFVIIa did not alter time to resolution of AH (P = .50), duration of mechanical ventilation (P = .89), duration of oxygen supplementation (P = .55), or hospital mortality (P = .27). Four possible thrombotic events (9% of 43 episodes) occurred with rFVIIa. rFVIIa in combination with corticosteroids did not confer clear clinical advantages compared with corticosteroids alone. In patients with AH following hematopoietic stem cell transplantation, clinical factors (ie, worsening infection, multiple organ failure, or recrudescence of primary disease) may be more important than the benefit of enhanced hemostasis from rFVIIa.

RETHINKING INTERMEDIATE LAYERS DESIGN IN KNOWLEDGE DISTILLATION FOR KIDNEY AND LIVER TUMOR SEGMENTATION.

Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not specifically tailored for these tasks. Moreover, prevalent KD methods often lack a careful consideration of 'what' and 'from where' to distill knowledge from the teacher to the student. This oversight may lead to issues like the accumulation of training bias within shallower student layers, potentially compromising the effectiveness of KD. To address these challenges, we propose Hierarchical Layer-selective Feedback Distillation (HLFD). HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels. This design allows the model to learn higher-quality representations from earlier layers, resulting in a robust and compact student model. Extensive quantitative evaluations reveal that HLFD outperforms existing methods by a significant margin. For example, in the kidney segmentation task, HLFD surpasses the student model (without KD) by over 10%, significantly improving its focus on tumor-specific features. From a qualitative standpoint, the student model trained using HLFD excels at suppressing irrelevant information and can focus sharply on tumor-specific details, which opens a new pathway for more efficient and accurate diagnostic tools. Code is available here.

Domain Generalization with Correlated Style Uncertainty.

Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://github.com/freshman97/CSU.

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges.

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a routine clinical examination. For the instrument segmentation task, the best team obtained a mean Intersection over union metric of 0.9364. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. The best team obtained a final transparency score of 21 out of 25. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage subjective evaluation for building more transparent and understandable AI-based colonoscopy systems. Moreover, we discuss the need for multi-center and out-of-distribution testing to address the current limitations of the methods to reduce the cancer burden and improve patient care.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

Selecting the best optimizers for deep learning-based medical image segmentation.

Purpose: The goal of this work is to explore the best optimizers for deep learning in the context of medical image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Approach: Most successful deep learning networks are trained using two types of stochastic gradient descent (SGD) algorithms: adaptive learning and accelerated schemes. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms [LR and momentum optimizers or momentum rate (MR) in short], in this article, we explore the two variants of SGD algorithms in a single setting. We suggest using cyclic learning as the base optimizer and integrating optimal values of learning rate and momentum rate. The new optimization function proposed in this work is based on the Nesterov accelerated gradient optimizer, which is more efficient computationally and has better generalization capabilities compared to other adaptive optimizers. Results: We investigated the relationship of LR and MR under an important problem of medical image segmentation of cardiac structures from MRI and CT scans. We conducted experiments using the cardiac imaging dataset from the ACDC challenge of MICCAI 2017, and four different architectures were shown to be successful for cardiac image segmentation problems. Our comprehensive evaluations demonstrated that the proposed optimizer achieved better results (over a 2% improvement in the dice metric) than other optimizers in the deep learning literature with similar or lower computational cost in both single and multi-object segmentation settings. Conclusions: We hypothesized that the combination of accelerated and adaptive optimization methods can have a drastic effect in medical image segmentation performances. To this end, we proposed a new cyclic optimization method (Cyclic Learning/Momentum Rate) to address the efficiency and accuracy problems in deep learning-based medical image segmentation. The proposed strategy yielded better generalization in comparison to adaptive optimizers.

An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation.

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset. Our code is available at https://github.com/NoviceMAn-prog/OARFocalFuse.