Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system

Emerging applications of machine learning in genomic medicine and healthcare

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

PURPOSE

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance.

METHODS

187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve.

RESULTS

The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05).

CONCLUSION

For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules

Background

Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework.

Methods

Two datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model.

Results

For the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods.

Conclusions

We realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification

Background

This study aimed (I) to investigate the clinical implication of computed tomography (CT) cavity volume in tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD), and (II) to develop a three-dimensional (3D) nnU-Net model to automatically detect and quantify cavity volume on CT images.

Methods

We retrospectively included conveniently sampled 206 TB and 186 NTM-PD patients in a tertiary referral hospital, who underwent thin-section chest CT scans from 2012 through 2019. TB was microbiologically confirmed, and NTM-PD was diagnosed by 2007 Infectious Diseases Society of America/American Thoracic Society guideline. The reference cavities were semi-automatically segmented on CT images and a 3D nnU-Net model was built with 298 cases (240 cases for training, 20 for tuning, and 38 for internal validation). Receiver operating characteristic curves were used to evaluate the accuracy of the CT cavity volume for two clinically relevant parameters: sputum smear positivity in TB and treatment in NTM-PD. The sensitivity and false-positive rate were calculated to assess the cavity detection of nnU-Net using radiologist-detected cavities as references, and the intraclass correlation coefficient (ICC) between the reference and the U-Net-derived cavity volumes was analyzed.

Results

The mean CT cavity volumes in TB and NTM-PD patients were 11.3 and 16.4 cm³, respectively, and were significantly greater in smear-positive TB (P<0.001) and NTM-PD necessitating treatment (P=0.020). The CT cavity volume provided areas under the curve of 0.701 [95% confidence interval (CI): 0.620-0.782] for TB sputum positivity and 0.834 (95% CI: 0.773-0.894) for necessity of NTM-PD treatment. The nnU-Net provided per-patient sensitivity of 100% (19/19) and per-lesion sensitivity of 83.7% (41/49) in the validation dataset, with an average of 0.47 false-positive small cavities per patient (median volume, 0.26 cm³). The mean Dice similarity coefficient between the manually segmented cavities and the U-Net-derived cavities was 78.9. The ICCs between the reference and U-Net-derived volumes were 0.991 (95% CI: 0.983-0.995) and 0.933 (95% CI: 0.897-0.957) on a per-patient and per-lesion basis, respectively.

Conclusions

CT cavity volume was associated with sputum positivity in TB and necessity of treatment in NTM-PD. The 3D nnU-Net model could automatically detect and quantify mycobacterial cavities on chest CT, helping assess TB infectivity and initiate NTM-TB treatment.

Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study

OBJECTIVE

To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department.

METHODS

Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3.

RESULTS

The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05).

CONCLUSIONS

DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules

Simple Summary

Pulmonary nodules are considered a sign of bronchogenic carcinoma, detecting them early will reduce their progression and can save lives. Lung cancer is the second most common type of cancer in both men and women. This manuscript discusses the current applications of artificial intelligence (AI) in lung segmentation as well as pulmonary nodule segmentation and classification using computed tomography (CT) scans, published in the last two decades, in addition to the limitations and future prospects in the field of AI.

Abstract

Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.

Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing

BACKGROUND

It is critical to have a deep learning-based system validated on an external dataset before it is used to assist clinical prognoses. The aim of this study was to assess the performance of an artificial intelligence (AI) system to detect tuberculosis (TB) in a large-scale external dataset.

METHODS

An artificial, deep convolutional neural network (DCNN) was developed to differentiate TB from other common abnormalities of the lung on large-scale chest X-ray radiographs. An internal dataset with 7,025 images was used to develop the AI system, including images were from five sources in the U.S. and China, after which a 6-year dynamic cohort accumulation dataset with 358,169 images was used to conduct an independent external validation of the trained AI system.

RESULTS

The developed AI system provided a delineation of the boundaries of the lung region with a Dice coefficient of 0.958. It achieved an AUC of 0.99 and an accuracy of 0.948 on the internal data set, and an AUC of 0.95 and an accuracy of 0.931 on the external data set when it was used to detect TB from normal images. The AI system achieved an AUC of more than 0.9 on the internal data set, and an AUC of over 0.8 on the external data set when it was applied to detect TB, non-TB abnormal and normal images.

CONCLUSIONS

We conducted a real-world independent validation, which showed that the trained system can be used as a TB screening tool to flag possible cases for rapid radiologic review and guide further examinations for radiologists.