A comparative study of an on premise AutoML solution for medical image classification.

Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Incorporating Reflective Learning Practices In Medical Imaging Curriculum.

PURPOSE: To provide an overview of the reflective learning cycle, as well as common reflective learning models, as a means of informing future implementation of reflective learning assignments in medical imaging curriculum. METHODS: Journal articles were searched for in Google Scholar, ScienceDirect, and ResearchGate, as well as the university's library databases using the keywords reflective learning, Kolb's model of learning, reflective learning practices in health care, and reflective learning in radiography. Out of 19 articles found, 12 articles were selected based on inclusion and exclusion criteria. RESULTS: The literature search yielded results in health care education, nursing, medicine, medical imaging and radiography, pharmacy, physical therapy, and occupational therapy. DISCUSSION: Studies have shown that reflection is an integral aspect of learning and has substantial implications for learners' clinical practice. Reflection is a cognitive process that facilitates learning, assists in the understanding and application of knowledge to clinical situations, and develops new clinical knowledge in student radiographers. When reflective activities, such as journaling, portfolios, and problem-based learning, are scaffolded throughout the curriculum, students develop critical reflection skills that positively affect their clinical practice. CONCLUSION: Reflective learning practices can positively affect student learning, clinical decision-making skills, and patient outcomes. When reflective learning activities are incorporated throughout the curriculum, students are more effectively able to bridge the gap between theoretical knowledge and clinical practice. In addition, the reflective learning process allows learners to examine their clinical experiences while providing context for application and future clinical practice and continued learning.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Current concepts in clinical features and diagnosis of thoracic outlet syndrome.

The diagnosis and clinical features of thoracic outlet syndrome have long confounded clinicians, owing to heterogeneity in symptom presentation and many overlapping competing diagnoses that are "more common." Despite the advent and prevalence of high-resolution imaging, along with the increasing awareness of the syndrome itself, misdiagnoses and untimely diagnoses can result in significant patient morbidity. The authors aimed to summarize the current concepts in the clinical features and diagnosis of thoracic outlet syndrome.

Neurogenic thoracic outlet syndrome and controversies in diagnosis and management.

Compression of the neurovascular structures at the level of the scalene triangle and pectoralis minor space is rare, but increasing awareness and understanding is allowing for the treatment of more individuals than in the past. We outlined the recognition, preoperative evaluation, and treatment of patients with neurogenic thoracic outlet syndrome. Recent work has illustrated the role of imaging and centrality of the physical examination on the diagnosis. However, a fuller understanding of the spatial biomechanics of the shoulder, scalene triangle, and pectoralis minor musculotendinous complex has shown that, although physical therapy is a mainstay of treatment, a poor response to physical therapy with a sound diagnosis should not preclude decompression. Modes of failure of surgical decompression stress the importance of full resection of the anterior scalene muscle and all posterior rib impinging elements to minimize the risk of recurrence of symptoms. Neurogenic thoracic outlet syndrome is a rare but critical cause of disability of the upper extremity. Modern understanding of the pathophysiology and evaluation have led to a sounder diagnosis. Although physical therapy is a mainstay, surgical decompression remains the gold standard to preserve and recover function of the upper extremity. Understanding these principles will be central to further developments in the treatment of this patient population.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification.

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment.

PURPOSE: In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. METHODS: With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. RESULTS: Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. CONCLUSION: Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

Analysis of invasive diagnostic techniques for pathological confirmation of pleural mesothelioma.

BACKGROUND AND OBJECTIVES: Mesothelioma is an infrequent neoplasm with a poor prognosis that is related to exposure to asbestos and whose peak incidence in Europe is estimated from 2020. Its diagnosis is complex; imaging techniques and the performance of invasive pleural techniques being essential for pathological confirmation. The different diagnostic yields of these invasive techniques are collected in the medical literature. The present work consisted of reviewing how the definitive diagnosis of mesothelioma cases in our centre was reached to check if there was concordance with the data in the bibliography. MATERIALS AND METHODS: Retrospective review of patients with a diagnosis of pleural mesothelioma in the period 2019-2021, analysing demographic data and exposure to asbestos, the semiology of the radiological findings and the invasive techniques performed to reach the diagnosis. RESULTS: Twenty-six mesothelioma cases were reviewed. 22 men and 4 women. Median age 74 years. 9 patients had a history of asbestos exposure. Moderate-severe pleural effusion was the most frequent radiological finding (23/26). The sensitivity of the invasive techniques was as follows: Cytology 13%, biopsy without image guidance 11%, image-guided biopsy 93%, surgical biopsy 67%. CONCLUSIONS: In our review, pleural biopsy performed with image guidance was the test that had the highest diagnostic yield, so it should be considered as the initial invasive test for the study of mesothelioma.

Vision, body and interpretation in medical imaging diagnostics.

This article explores the profound impact of visualism and visual perception in the context of medical imaging diagnostics. It emphasizes the intricate interplay among vision, embodiment, subjectivity, language, and historicity within the realm of medical science and technology, with a specific focus on image consciousness. The study delves into the role of subjectivity in perception, facilitating the communication of opacity and historicity to the perceiving individual. Additionally, it scrutinizes the image interpretation process, drawing parallels to text interpretation and highlighting the influence of personal biases and individuality in medical practice. By revisiting Husserl's conceptualization of "image consciousness" and introducing the notion of "image theme", the paper seeks to establish a theoretical framework for making sense of images within the context of technological interpretation. A key objective is to enhance the phenomenology of technology through a systematic analysis of medical imaging diagnosis, contributing to an expanded epistemological foundation for medical practice. The article recognizes that the construction of medical knowledge incorporates subjective elements, especially within a historical context. The interpretation of images involves both instrumental and expert interpretation, with human subjectivity playing a crucial role. The article asserts that human creativity and conscious engagement are indispensable in interpreting all medical images.

The correlation between ultrasonographic findings and clinical symptoms of pelvic endometriosis.

OBJECTIVE: Considering the importance of endometriosis and its relatively high prevalence among women, this study sought to investigate clinical and Transrectal and transvaginal ultrasounds (TVS) findings of disease. METHODS: This descriptive-analytical study was performed based on medical records of 155 women with endometriosis admitted to Rasool-e Akram Hospital in Tehran for a TVS. All the sonography data and patients' information were collected into checklists and analyzed in SPSS-25 software (IBM). RESULTS: The mean age of participants was 32.4 ± 6.1 years, ranging from 18 to 50 years. Endometrioma was prevalent in 129 patients (84.8%). Size of endometrioma (diameter) was more than 3 cm in 79.9% of patients, and 3 cm or fewer in 20.1% of cases. Bladder, intestinal, vaginal, and rectosigmoid involvements with endometriosis implants were observed in 4 (2.6%), 54 (35.5), 3 (0.2%), and 51 (33.5) of patients, respectively. A total of 64.5% of patients were diagnosed with incomplete stenosis of the Douglas pouch and 35.5% had complete stenosis. Deep infiltrating endometriosis (DIE) was less than 1 cm in 20.7%, 1 to 3 cm in 42.3%, and over 3 cm in 37% of patients. The most common manifestations of endometriosis Obliteration of the Douglas pouch, endometrioma, and DIE. In addition, imaging modalities have shown promising results, indicating the necessity to use transvaginal ultrasound as the first line of diagnosis in patients with clinically suspected endometriosis.

Biomimetic NIR-II fluorescent proteins created from chemogenic protein-seeking dyes for multicolor deep-tissue bioimaging.

Near-infrared-I/II fluorescent proteins (NIR-I/II FPs) are crucial for in vivo imaging, yet the current NIR-I/II FPs face challenges including scarcity, the requirement for chromophore maturation, and limited emission wavelengths (typically < 800 nm). Here, we utilize synthetic protein-seeking NIR-II dyes as chromophores, which covalently bind to tag proteins (e.g., human serum albumin, HSA) through a site-specific nucleophilic substitution reaction, thereby creating proof-of-concept biomimetic NIR-II FPs. This chemogenic protein-seeking strategy can be accomplished under gentle physiological conditions without catalysis. Proteomics analysis identifies specific binding site (Cys 477 on DIII). NIR-II FPs significantly enhance chromophore brightness and photostability, while improving biocompatibility, allowing for high-performance NIR-II lymphography and angiography. This strategy is universal and applicable in creating a wide range of spectrally separated NIR-I/II FPs for real-time visualization of multiple biological events. Overall, this straightforward biomimetic approach holds the potential to transform fluorescent protein-based bioimaging and enables in-situ albumin targeting to create NIR-I/II FPs for deep-tissue imaging in live organisms.

Transgender and gender diverse curriculum in medical imaging programs: a case study.

BACKGROUND: Transgender and gender diverse (TGD) individuals face barriers, including harassment and discrimination, when accessing healthcare services. Medical imaging procedures require personal information to be shared, such as date of last menstrual cycle and/or pregnancy status; some imaging exams are also invasive or intimate in nature. Terminology is based on binary sex creating an inherently cis-heteronormative environment. TGD patients fear being outed and often feel a need to function as educators and advocates for their care. Incorporation of inclusive healthcare curriculum related to TGD populations is an effective means of educating new health providers and promotes safer and more inclusive spaces in healthcare settings. Educators face barriers which hinder the creation and implementation of TGD content. The purpose of this study was to examine the impacts educators are faced with when creating and delivering TGD content in their medical imaging curriculum. METHODS: A case study of medical imaging programs at a Canadian post-secondary institute was undertaken. Data was collected via semi-structured interviews with faculty. Relevant institutional documents such as strategic plans, policies/procedures, websites, and competency profiles were accessed. Framework analysis was used to analyze the data. RESULTS: The study found seven themes that influence the development of TGD curriculum as follows: familiarity and comfort with the curriculum and content change process; collaboration with other healthcare programs; teaching expertise; management of course workload and related. duties; connections to the TGD community; knowledge of required TGD content and existing gaps in curriculum; and access to supports. CONCLUSIONS: Understanding educators' perspectives can lead to an increased sense of empowerment for them to create and incorporate TGD curriculum in the future. Many post- secondary institutions are incorporating an inclusive lens to educational plans; this research can be used in future curriculum design projects. The goal is improved medical imaging experiences for the TGD population.

Image testing based on biomedical drug loading in cardiovascular drug care simulation for coronary heart disease.

Coronary heart disease is a common cardiovascular disease, and its therapeutic effect is affected by the distribution and absorption of drugs in the body. Biomedical drug-carrying image testing technology can provide a quantitative assessment of drug distribution and absorption in the body. This study aims to explore the application of biomedical drug-carrying image testing technology in the simulation of cardiovascular drug care in coronary heart disease, so as to provide reference for the optimization of drug treatment plan and individualized treatment. The study collected clinical data and medication regiments of patients with coronary heart disease. Then, the imaging examination of patients was carried out by selecting appropriate drug loading markers using the biomedical drug loading image examination technology. Then quantitative analysis was used to process the image data to quantitatively evaluate the distribution and absorption of drugs in the cardiovascular system. The quantitative data of drug distribution and absorption in patients with coronary heart disease have been obtained successfully by means of biomedical imaging. These data reveal the dynamic changes of drugs in the cardiovascular system, and help doctors optimize drug therapy, improve treatment effectiveness, and achieve personalized treatment.

Multicore fiber optic imaging reveals that astrocyte calcium activity in the mouse cerebral cortex is modulated by internal motivational state.

Astrocytes are a direct target of neuromodulators and can influence neuronal activity on broad spatial and temporal scales in response to a rise in cytosolic calcium. However, our knowledge about how astrocytes are recruited during different animal behaviors remains limited. To measure astrocyte activity calcium in vivo during normative behaviors, we utilize a high-resolution, long working distance multicore fiber optic imaging system that allows visualization of individual astrocyte calcium transients in the cerebral cortex of freely moving mice. We define the spatiotemporal dynamics of astrocyte calcium changes during diverse behaviors, ranging from sleep-wake cycles to the exploration of novel objects, showing that their activity is more variable and less synchronous than apparent in head-immobilized imaging conditions. In accordance with their molecular diversity, individual astrocytes often exhibit distinct thresholds and activity patterns during explorative behaviors, allowing temporal encoding across the astrocyte network. Astrocyte calcium events were induced by noradrenergic and cholinergic systems and modulated by internal state. The distinct activity patterns exhibited by astrocytes provides a means to vary their neuromodulatory influence in different behavioral contexts and internal states.