Evaluation of computer aided detection during colonoscopy among Veterans: Randomized clinical trial

BACKGROUND

There has been significant interest in use of computer aided detection (CADe) devices in colonoscopy to improve polyp detection and reduce miss rate.

AIM

To investigate the use of CADe amongst veterans.

METHODS

Between September 2020 and December 2021, we performed a randomized controlled trial to evaluate the impact of CADe. Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe. Primary outcomes of interest included adenoma detection rate (ADR), adenomas per colonoscopy (APC), and adenomas per extraction. In addition, we measured serrated polyps per colonoscopy, non-adenomatous, non-serrated polyps per colonoscopy, serrated polyp detection rate, and procedural time.

RESULTS

A total of 244 patients were enrolled (124 with CADe), with similar patient characteristics (age, sex, body mass index, indication) between the two groups. Use of CADe was found to have decreased number of adenomas (1.79 vs 2.53, P = 0.030) per colonoscopy compared to without CADe. There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups. Overall, use of CADe was found to have lower ADR (68.5% vs 80.0%, P = 0.041) compared to without use of CADe. Serrated polyp detection rate was lower with CADe (3.2% vs 7.5%) compared to without CADe, but this was not statistically significant (P = 0.137). There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction (71.4% vs 73.1%, P = 0.613). No adverse events were identified.

CONCLUSION

While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe, in this RCT performed at a center with high ADR, use of CADe was found to have decreased APC and ADR. Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

Radiomics: Status quo and future challenges

Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.

Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease

The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.

Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights

Much of the published literature in Radiology-related Artificial Intelligence (AI) focuses on single tasks, such as identifying the presence or absence or severity of specific lesions. Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image. Also, since a diagnosis is rarely achieved through an image alone, radiology AI must be able to employ diverse strategies that consider all available evidence, not just imaging information. Using key imaging and clinical signs will help improve their accuracy and utility tremendously. Employing strategies that consider all available evidence will be a formidable task; we believe that the combination of human and computer intelligence will be superior to either one alone. Further, unless an AI application is explainable, radiologists will not trust it to be either reliable or bias-free; we discuss some approaches aimed at providing better explanations, as well as regulatory concerns regarding explainability (“transparency”). Finally, we look at federated learning, which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models, and quantum computing, still prototypical but potentially revolutionary in its computing impact.

Artificial intelligence: Advances and new frontiers in medical imaging

Artificial intelligence (AI) has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago. These algorithms, ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks, have continuously sought to revolutionize medical imaging. With the number of imaging studies outpacing the amount of trained of readers, AI has been implemented to streamline workflow efficiency and provide quantitative, standardized interpretation. AI relies on massive amounts of data for its algorithms to function, and with the wide-spread adoption of Picture Archiving and Communication Systems (PACS), imaging data is accumulating rapidly. Current AI algorithms using machine-learning technology, or computer aided-detection, have been able to successfully pool this data for clinical use, although the scope of these algorithms remains narrow. Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage, however interpretation has generally remained a human responsibility for now. In this review article, we will summarize the current successes and limitations of AI in radiology, and explore the exciting prospects that deep-learning technology offers for the future.

Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma

Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.

Applications of artificial intelligence in lung ultrasound: Review of deep learning methods for COVID-19 fighting

BACKGROUND

The pandemic outbreak of the novel coronavirus disease (COVID-19) has highlighted the need to combine rapid, non-invasive and widely accessible techniques with the least risk of patient’s cross-infection to achieve a successful early detection and surveillance of the disease. In this regard, the lung ultrasound (LUS) technique has been proved invaluable in both the differential diagnosis and the follow-up of COVID-19 patients, and its potential may be destined to evolve. Recently, indeed, LUS has been empowered through the development of automated image processing techniques.

AIM

To provide a systematic review of the application of artificial intelligence (AI) technology in medical LUS analysis of COVID-19 patients using the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines.

METHODS

A literature search was performed for relevant studies published from March 2020 - outbreak of the pandemic - to 30 September 2021. Seventeen articles were included in the result synthesis of this paper.

RESULTS

As part of the review, we presented the main characteristics related to AI techniques, in particular deep learning (DL), adopted in the selected articles. A survey was carried out on the type of architectures used, availability of the source code, network weights and open access datasets, use of data augmentation, use of the transfer learning strategy, type of input data and training/test datasets, and explainability.

CONCLUSION

Finally, this review highlighted the existing challenges, including the lack of large datasets of reliable COVID-19-based LUS images to test the effectiveness of DL methods and the ethical/regulatory issues associated with the adoption of automated systems in real clinical scenarios.

Chest ultrasound in neonates: What neonatologists should know

For many years, ultrasound was thought to have no indications in pulmonary imaging because lungs are filled with air, creating no acoustic mismatch, as encountered by ultrasound wave beam. Lung ultrasound (LUS) was started in adult critical care settings to detect pleural effusion and acquired more indications over time. In the neonatal intensive care unit (NICU), the use of chest ultrasound has gained more attention during the last two decades. Being a radiation-free, bedside, rapid, and handy tool, LUS started to replace chest X-rays in NICU. Using LUS depends upon understanding the nature of normal lungs and the changes induced by different diseases. With the help of LUS, an experienced neonatologist can detect many of the respiratory problems so fast that interventional therapy can be introduced as early as possible. LUS can diagnose pleural effusion, pneumothorax, pneumonia, transient tachypnoea of the newborn, respiratory distress syndrome, pulmonary atelectasis, meconium aspiration syndrome, bronchopulmonary dysplasia, and some other disorders with very high accuracy. LUS will be helpful in initial diagnosis, follow-up, and predicting the need for further procedures such as mechanical ventilation, diuretic therapy, surfactant therapy, etc. There are some limitations to using LUS in some respiratory disorders such as bullae, interstitial emphysema, and other conditions. This review will highlight the importance of LUS, its uses, and limitations.

Applications of artificial intelligence in common pulmonary diseases

Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.

Pictorial research of pancreas with artificial intelligence and simulacra in the works of Fellini

This is the consideration recalled from my reading of Acute pancreatitis: A pictorial review of early pancreatic fluid collections by Xiao. This perspective related to the works of Fellini might be able to contribute the future development of the research of pancreatic diseases.

Application of machine learning in oral and maxillofacial surgery

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.

Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions

Since its inception in 1959, artificial intelligence (AI) has evolved at an unprecedented rate and has revolutionized the world of medicine. Ophthalmology, being an image-driven field of medicine, is well-suited for the implementation of AI. Machine learning (ML) and deep learning (DL) models are being utilized for screening of vision threatening ocular conditions of the eye. These models have proven to be accurate and reliable for diagnosing anterior and posterior segment diseases, screening large populations, and even predicting the natural course of various ocular morbidities. With the increase in population and global burden of managing irreversible blindness, AI offers a unique solution when implemented in clinical practice. In this review, we discuss what are AI, ML, and DL, their uses, future direction for AI, and its limitations in ophthalmology.

Current status of deep learning in abdominal image reconstruction

Abdominal magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for disease screening, diagnosis, and treatment guidance. However, abdominal MRI has disadvantages including slow speed and vulnerability to motions, while CT suffers from problems of radiation. It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality. Recently, deep learning-based image reconstruction has become a hot topic in the field of medical imaging. This study reviews the latest research on deep learning reconstruction in abdominal imaging, including the widely used convolutional neural network, generative adversarial network, and recurrent neural network.

Application of radiomics in hepatocellular carcinoma: A review

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.

Artificial intelligence in coronary computed tomography angiography

Coronary computed tomography angiography (CCTA) is recommended as a frontline diagnostic tool in the non-invasive assessment of patients with suspected coronary artery disease (CAD) and cardiovascular risk stratification. To date, artificial intelligence (AI) techniques have brought major changes in the way that we make individualized decisions for patients with CAD. Applications of AI in CCTA have produced improvements in many aspects, including assessment of stenosis degree, determination of plaque type, identification of high-risk plaque, quantification of coronary artery calcium score, diagnosis of myocardial infarction, estimation of computed tomography-derived fractional flow reserve, left ventricular myocardium analysis, perivascular adipose tissue analysis, prognosis of CAD, and so on. The purpose of this review is to provide a comprehensive overview of current status of AI in CCTA.

Implementation of lung ultrasound in the triage of pregnant women during the SARS-CoV-2 pandemics

Lung ultrasound (US) has been shown that it is able to detect interstitial lung disease, subpleural consolidations and acute respiratory distress syndrome in clinical and physical studies that assess its role in upper respiratory infections. It is used worldwide in the coronavirus disease 2019 (COVID-19) outbreak and the effectiveness has been assessed in several studies. Fast diagnosis of COVID-19 is essential in deciding for patient isolation, clinical care and reducing transmission. Imaging the lung and pleura by ultrasound is efficient, cost-effective, and safe and it is recognized as rapid, repeatable, and reliable. Obstetricians are already using the US and are quite proficient in doing so. During the pandemic, performing lung US (LUS) right after the fetal assessment until reverse transcription polymerase chain reaction results are obtained, particularly in settings that have a centralized testing center, was found feasible for the prediction of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The use of LUS is efficient in the triage and monitoring of pregnant women. Clinicians dealing with pregnant women should consider LUS as the first-line diagnostic tool in pregnant women during the SARS-CoV-2 pandemic.

Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy

Artificial intelligence (AI) has seen tremendous growth over the past decade and stands to disrupts the medical industry. In medicine, this has been applied in medical imaging and other digitised medical disciplines, but in more traditional fields like medical physics, the adoption of AI is still at an early stage. Though AI is anticipated to be better than human in certain tasks, with the rapid growth of AI, there is increasing concerns for its usage. The focus of this paper is on the current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Topics on AI for image acquisition, image segmentation, treatment delivery, quality assurance and outcome prediction will be explored as well as the interaction between human and AI. This will give insights into how we should approach and use the technology for enhancing the quality of clinical practice.

Artificial intelligence in radiation oncology

Artificial intelligence (AI) is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input. Machine learning (ML) is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience. Deep learning is an ML technique that uses deep neural networks to create a model. The growth and sharing of data, increasing computing power, and developments in AI have initiated a transformation in healthcare. Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging, dosimetry, and imaging performed before each fraction. Of the many algorithms used in radiation oncology, has advantages and limitations with different computational power requirements. The aim of this review is to summarize the radiotherapy (RT) process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML. The RT stage is divided into seven stages: patient evaluation, simulation, contouring, planning, quality control, treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations, and advantages of AI algorithms has been done for each stage.

Intrathyroidal ectopic thymus: Ultrasonographic features and differential diagnosis

Intrathyroidal ectopic thymus (IET) is defined as an ectopic thymus tissue that is generally found incidentally and rarely in the thyroid gland in the pediatric group. It occurs as a result of disruption of the embryological migration path and the settling of the thymus tissue into the thyroid gland. In the differential diagnosis, it is mostly confused with thyroid nodules. Although thyroid nodules are less common in children than adults, the rate of malignancy is much higher. Therefore, knowing the general ultrasound findings of IET better may prevent unnecessary invasive attempts and surgical procedures. In this article, we tried to compile the key imaging findings of IET.

Artificial intelligence in ophthalmology: A new era is beginning

The use of artificial intelligence (AI) in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma, thereby helping ophthalmologists to diagnose and treat diseases better than before. Incorporating “deep learning” (a subfield of AI) into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately. Similar applications have been tried in the field of retinopathy of prematurity and age-related macular degeneration, a silent retinal condition that needs to be diagnosed early to prevent progression. The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role. The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions. However, there are few significant challenges in the AI systems, such as the incorporation of quality images, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving the machines/software to potentiate their efficacy and standards. This review article shall discuss the current applications of AI in ophthalmology, significant challenges and the prospects as to how both science and medicine can work together.

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging (AIMI), it is our great pleasure to take this opportunity to wish all of our authors, subscribers, readers, Editorial Board members, independent expert referees, and staff of the Editorial Office a Very Happy New Year. On behalf of the Editorial Team, we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts, our independent referees, and our subscribers and readers for their continuous support, dedication, and encouragement. Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office, AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Current trends of artificial intelligence in cancer imaging

In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters

BACKGROUND

The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine. When the selected blastocyst is transferred to the uterus, the degree of implantation of the blastocyst is evaluated by microscopic inspection, and the result is only about 30%-40%, and the method of predicting live birth from the blastocyst image is unknown. Live births correlate with several clinical conventional embryo evaluation parameters (CEE), such as maternal age. Therefore, it is necessary to develop artificial intelligence (AI) that combines blastocyst images and CEE to predict live births.

AIM

To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.

METHODS

A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option. We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.

RESULTS

The live birth rate was 0.387 (= 1587/4104 cases). The number of independent clinical information for predicting live birth is 10, which significantly avoids multicollinearity. A single AI classifier is composed of ten layers of convolutional neural networks, and each elementwise layer of ten factors is developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value. The accuracy, sensitivity, specificity, negative predictive value, positive predictive value, Youden J index, and area under the curve values for predicting live birth are 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively. The optimal cut-off point of the receiver operator characteristic curve is 0.207.

CONCLUSION

AI classifiers have the potential of predicting live births that humans cannot predict. Artificial intelligence may make progress in assisted reproductive technology.

Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence

Optical molecular tomography (OMT) is an imaging modality which uses an optical signal, especially near-infrared light, to reconstruct the three-dimensional information of the light source in biological tissue. With the advantages of being low-cost, noninvasive and having high sensitivity, OMT has been applied in preclinical and clinical research. However, due to its serious ill-posedness and ill-condition, the solution of OMT requires heavy data analysis and the reconstruction quality is limited. Recently, the artificial intelligence (commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem. In this paper, we review the progress on OMT algorithms, from conventional methods to AI-based methods, and we also give a prospective towards future developments in this domain.

Artificial intelligence and pituitary adenomas: A review

The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.

Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey

Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.

Acoustic concept based on an autonomous capsule and a wideband concentric ring resonator for pathophysiological prevention

BACKGROUND

Research on the performance of elements constituting our modern environment is constantly evolving, both on a daily basis and on technological basis. But to date, the response of the system to the expectations of the population remains too modest.

AIM

To elaborate an ultrasonic technique to scan and evaluate in-vivo physiological properties by coupling sensors and multilayer biological tissues model.

METHODS

A low-frequency ultrasonic method (around a frequency of 32 KHz) based on the use of an innovative autonomous ultrasonic capsule as a miniaturized elementary spherical sensor (1 cm of diameter) and micro-rings resonators were examined.

RESULTS

Other their functions as passive listeners for the prevention and diagnosis in physiopathology of the respiratory and laryngeal apparatus, these micro-resonators coupled to the ultrasonic capsule through biological tissues (the body) are capable of evaluating the effects of aggression of the environment on human metabolism.

CONCLUSION

This would allow consequently the detection of some potential diseases at an early stage, even in people who still represent no symptoms, which would permit an early treatment and a higher chance of cure.

Acute pancreatitis: A pictorial review of early pancreatic fluid collections

Acute pancreatitis is a common acute inflammatory disease involving the pancreas and peripancreatic tissues or remote organs. The revised Atlanta classification 2012 of acute pancreatitis divides patients into mild, moderately severe and severe groups. Major changes of the classification include acute fluid collection terminology. However, some inappropriate terms of the radiological diagnosis reports in the daily clinical work or available literature may still be found. The aim of this review article is: to present an image-rich overview of different morphologic characteristics of the early-stage (within 4 wk after symptom onset) local complications associated with acute pancreatitis by computed tomography or magnetic resonance imaging; to clarify confusing imaging concepts for pancreatic fluid collections and underline standardised reporting nomenclature; to assist communication among treating physicians; and to facilitate the implications for clinical management decision-making.

Cerebral amyloid angiopathy vs Alzheimer’s dementia: Diagnostic conundrum

BACKGROUND

Diagnosis of a dementia subtype can be complex and often requires comprehensive cognitive assessment and dedicated neuroimaging. Clinicians are prone to cognitive biases when reviewing such images. We present a case of cognitive impairment and demonstrate that initial imaging may have resulted in misleading the diagnosis due to such cognitive biases.

CASE SUMMARY

A 76-year-old man with no cognitive impairment presented with acute onset word finding difficulty with unremarkable blood tests and neurological examination. Magnetic resonance imaging (MRI) demonstrated multiple foci of periventricular and subcortical microhaemorrhage, consistent with cerebral amyloid angiopathy (CAA). Cognitive assessment of this patient demonstrated marked impairment mainly in verbal fluency and memory. However, processing speed and executive function are most affected in CAA, whereas episodic memory is relatively preserved, unlike in other causes of cognitive impairment, such as Alzheimer’s dementia (AD). This raised the question of an underlying diagnosis of dementia. Repeat MRI with dedicated coronal views demonstrated mesial temporal lobe atrophy which is consistent with AD.

CONCLUSION

MRI brain can occasionally result in diagnostic overshadowing, and the application of artificial intelligence to medical imaging may overcome such cognitive biases.

Artificial intelligence in pancreatic disease

In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.

Rising role of artificial intelligence in image reconstruction for biomedical imaging

In this editorial, we review recent progress on the applications of artificial intelligence (AI) in image reconstruction for biomedical imaging. Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning, AI technology plays an increasingly important role in biomedical image reconstruction. The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field. Favoring AI, the performance of biomedical image reconstruction has been improved in terms of accuracy, resolution, imaging speed, etc. We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction, and propose possible future directions in this field.

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.