Today's radiologists meet tomorrow's AI: the promises, pitfalls, and unbridled potential

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BACKGROUND

To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.

METHODS

The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.

RESULTS

There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.

CONCLUSION

The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

The Role of Artificial Intelligence in Cardiac Imaging

Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.

Artificial intelligence in coronary computed tomography angiography: Demands and solutions from a clinical perspective

Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.

A narrative review: narrow-band imaging endoscopic classifications

Background and Objective

With the development of endoscopic techniques, narrow-band imaging (NBI) has been widely used in the diagnosis of various types of diseases. NBI can detect mucosal lesions at an early stage and different classification strategies have been established to help clinicians in disease diagnosis. However, there is currently no consensus for the classification criteria. This report summarizes the current classifications of diseases using NBI, so as to provide a comprehensive understanding of the various manifestations of mucosal lesions under NBI, and to promote the development of more practical NBI classifications.

Methods

The PubMed database was searched for English language articles published between January 1994 and November 2021 using the keywords 'narrow band imaging', 'NBI', and 'classification'.

Key Content and Findings

We systematically summarized the NBI classifications and manifestations of different diseases. The morphology of the mucosa and vessels was used as the basis of most classifications. These classifications are mainly helpful to distinguish benign and malignant tumors and to detect early neoplastic lesions.

Conclusions

This review summarized existing NBI classifications for different systems. These classifications will be updated as the understanding of diseases increases and new optical techniques become available to better assist doctors in making clinical decisions.

Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint

Background

The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods.

Methods

The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities.

Results

The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90-0.96. The accuracy of LeNet-5 for classifying classes 0-2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0-2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0-2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0-2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy.

Conclusions

Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.

The use of deep learning technology for the detection of optic neuropathy

The emergence of computer graphics processing units (GPUs), improvements in mathematical models, and the availability of big data, has allowed artificial intelligence (AI) to use machine learning and deep learning (DL) technology to achieve robust performance in various fields of medicine. The DL system provides improved capabilities, especially in image recognition and image processing. Recent progress in the sorting of AI data sets has stimulated great interest in the development of DL algorithms. Compared with subjective evaluation and other traditional methods, DL algorithms can identify diseases faster and more accurately in diagnostic tests. Medical imaging is of great significance in the clinical diagnosis and individualized treatment of ophthalmic diseases. Based on the morphological data sets of millions of data points, various image-related diagnostic techniques can now impart high-resolution information on anatomical and functional changes, thereby providing unprecedented insights in ophthalmic clinical practice. As ophthalmology relies heavily on imaging examinations, it is one of the first medical fields to apply DL algorithms in clinical practice. Such algorithms can assist in the analysis of large amounts of data acquired from the examination of auxiliary images. In recent years, rapid advancements in imaging technology have facilitated the application of DL in the automatic identification and classification of pathologies that are characteristic of ophthalmic diseases, thereby providing high quality diagnostic information. This paper reviews the origins, development, and application of DL technology. The technical and clinical problems associated with building DL systems to meet clinical needs and the potential challenges of clinical application are discussed, especially in relation to the field of optic nerve diseases.

Model-based clinical note entity recognition for rheumatoid arthritis using bidirectional encoder representation from transformers

Background

Rheumatoid arthritis (RA) is a disease of the immune system with a high rate of disability and there are a large amount of valuable disease diagnosis and treatment information in the clinical note of the electronic medical record. Artificial intelligence methods can be used to mine useful information in clinical notes effectively. This study aimed to develop an effective method to identify and classify medical entities in the clinical notes relating to RA and use the entity identification results in subsequent studies.

Methods

In this paper, we introduced the bidirectional encoder representation from transformers (BERT) pre-training model to enhance the semantic representation of word vectors. The generated word vectors were then inputted into the model, which is composed of traditional bidirectional long short-term memory neural networks and conditional random field machine learning algorithms for the named entity recognition of clinical notes to improve the model's effectiveness. The BERT method takes the combination of token embeddings, segment embeddings, and position embeddings as the model input and fine-tunes the model during training.

Results

Compared with the traditional Word2vec word vector model, the performance of the BERT pre-training model to obtain a word vector as model input was significantly improved. The best F1-score of the named entity recognition task after training using many rheumatoid arthritis clinical notes was 0.936.

Conclusions

This paper confirms the effectiveness of using an advanced artificial intelligence method to carry out named entity recognition tasks on a corpus of a large number of clinical notes; this application is promising in the medical setting. Moreover, the extraction of results in this study provides a lot of basic data for subsequent tasks, including relation extraction, medical knowledge graph construction, and disease reasoning.

Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging

BACKGROUND

Gradient-recalled echo (GRE) sequence is time-consuming and not routinely performed. Herein, we aimed to investigate the ability of weakly supervised learning to identify acute ischemic stroke (AIS) and concurrent hemorrhagic infarction based on diffusion-weighted imaging (DWI).

METHODS

First, we proposed spatially locating small stroke lesions in different positions and hemorrhagic infarction lesions by residual neural and visual geometry group networks using weakly supervised learning. Next, we compared the sensitivity and specificity for identifying automatically concurrent hemorrhagic infarction in stroke patients with the sensitivity and specificity of human readings of diffusion and b₀ images to evaluate the performance of the weakly supervised methods. Also, the labeling time of the weakly supervised approach was compared with that of the fully supervised approach.

RESULTS

Data from a total of 1,027 patients were analyzed. The residual neural network displayed a higher sensitivity than did the visual geometry group network in spatially locating the small stroke and hemorrhagic infarction lesions. The residual neural network had significantly greater patient-level sensitivity than did the human readers (98.4% versus 86.2%, P=0.008) in identifying concurrent hemorrhagic infarction with GRE as the reference standard; however, their specificities were comparable (95.4% versus 96.9%, P>0.99). Weak labeling of lesions required significantly less time than did full labeling of lesions (2.667 versus 10.115 minutes, P<0.001).

CONCLUSIONS

Weakly supervised learning was able to spatially locate small stroke lesions in different positions and showed more sensitivity than did human reading in identifying concurrent hemorrhagic infarction based on DWI. The proposed approach can reduce the labeling workload.