Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions

Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of our daily lives, and cardiovascular medicine is no exception. Here, we provide physicians with an overview of the past, present, and future of artificial intelligence applications in cardiovascular medicine. We describe essential and powerful examples of machine-learning applications in industry and elsewhere. Finally, we discuss the latest technologic advances, as well as the benefits and limitations of artificial intelligence and machine learning in cardiovascular medicine.

Artificial Intelligence in Echocardiography

Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex echocardiographic imaging techniques and expanded use among noncardiologists. Here, we provide an overview of real-world applications of artificial intelligence in echocardiography including automatic high-quality computer-optimized image acquisition sequences, automated measurements, and algorithms for the rapid and accurate interpretation of cardiac physiology. These advances will not replace physicians but will improve their productivity, workflow, and diagnostic performance.

Artificial intelligence for the management of pancreatic diseases

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Background

According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis.

Objective

The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management.

Methods

To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3).

Results

Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021.

Conclusions

The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16047

Ultrasound, CT, MRI, or PET-CT for staging and re-staging of adults with cutaneous melanoma

BACKGROUND

Melanoma is one of the most aggressive forms of skin cancer, with the potential to metastasise to other parts of the body via the lymphatic system and the bloodstream. Melanoma accounts for a small percentage of skin cancer cases but is responsible for the majority of skin cancer deaths. Various imaging tests can be used with the aim of detecting metastatic spread of disease following a primary diagnosis of melanoma (primary staging) or on clinical suspicion of disease recurrence (re-staging). Accurate staging is crucial to ensuring that patients are directed to the most appropriate and effective treatment at different points on the clinical pathway. Establishing the comparative accuracy of ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT imaging for detection of nodal or distant metastases, or both, is critical to understanding if, how, and where on the pathway these tests might be used.

OBJECTIVES

Primary objectivesWe estimated accuracy separately according to the point in the clinical pathway at which imaging tests were used. Our objectives were:• to determine the diagnostic accuracy of ultrasound or PET-CT for detection of nodal metastases before sentinel lymph node biopsy in adults with confirmed cutaneous invasive melanoma; and• to determine the diagnostic accuracy of ultrasound, CT, MRI, or PET-CT for whole body imaging in adults with cutaneous invasive melanoma:○ for detection of any metastasis in adults with a primary diagnosis of melanoma (i.e. primary staging at presentation); and○ for detection of any metastasis in adults undergoing staging of recurrence of melanoma (i.e. re-staging prompted by findings on routine follow-up).We undertook separate analyses according to whether accuracy data were reported per patient or per lesion.Secondary objectivesWe sought to determine the diagnostic accuracy of ultrasound, CT, MRI, or PET-CT for whole body imaging (detection of any metastasis) in mixed or not clearly described populations of adults with cutaneous invasive melanoma.For study participants undergoing primary staging or re-staging (for possible recurrence), and for mixed or unclear populations, our objectives were:• to determine the diagnostic accuracy of ultrasound, CT, MRI, or PET-CT for detection of nodal metastases;• to determine the diagnostic accuracy of ultrasound, CT, MRI, or PET-CT for detection of distant metastases; and• to determine the diagnostic accuracy of ultrasound, CT, MRI, or PET-CT for detection of distant metastases according to metastatic site.

SEARCH METHODS

We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists as well as published systematic review articles.

SELECTION CRITERIA

We included studies of any design that evaluated ultrasound (with or without the use of fine needle aspiration cytology (FNAC)), CT, MRI, or PET-CT for staging of cutaneous melanoma in adults, compared with a reference standard of histological confirmation or imaging with clinical follow-up of at least three months' duration. We excluded studies reporting multiple applications of the same test in more than 10% of study participants.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2)). We estimated accuracy using the bivariate hierarchical method to produce summary sensitivities and specificities with 95% confidence and prediction regions. We undertook analysis of studies allowing direct and indirect comparison between tests. We examined heterogeneity between studies by visually inspecting the forest plots of sensitivity and specificity and summary receiver operating characteristic (ROC) plots. Numbers of identified studies were insufficient to allow formal investigation of potential sources of heterogeneity.

MAIN RESULTS

We included a total of 39 publications reporting on 5204 study participants; 34 studies reporting data per patient included 4980 study participants with 1265 cases of metastatic disease, and seven studies reporting data per lesion included 417 study participants with 1846 potentially metastatic lesions, 1061 of which were confirmed metastases. The risk of bias was low or unclear for all domains apart from participant flow. Concerns regarding applicability of the evidence were high or unclear for almost all domains. Participant selection from mixed or not clearly defined populations and poorly described application and interpretation of index tests were particularly problematic.The accuracy of imaging for detection of regional nodal metastases before sentinel lymph node biopsy (SLNB) was evaluated in 18 studies. In 11 studies (2614 participants; 542 cases), the summary sensitivity of ultrasound alone was 35.4% (95% confidence interval (CI) 17.0% to 59.4%) and specificity was 93.9% (95% CI 86.1% to 97.5%). Combining pre-SLNB ultrasound with FNAC revealed summary sensitivity of 18.0% (95% CI 3.58% to 56.5%) and specificity of 99.8% (95% CI 99.1% to 99.9%) (1164 participants; 259 cases). Four studies demonstrated lower sensitivity (10.2%, 95% CI 4.31% to 22.3%) and specificity (96.5%,95% CI 87.1% to 99.1%) for PET-CT before SLNB (170 participants, 49 cases). When these data are translated to a hypothetical cohort of 1000 people eligible for SLNB, 237 of whom have nodal metastases (median prevalence), the combination of ultrasound with FNAC potentially allows 43 people with nodal metastases to be triaged directly to adjuvant therapy rather than having SLNB first, at a cost of two people with false positive results (who are incorrectly managed). Those with a false negative ultrasound will be identified on subsequent SLNB.Limited test accuracy data were available for whole body imaging via PET-CT for primary staging or re-staging for disease recurrence, and none evaluated MRI. Twenty-four studies evaluated whole body imaging. Six of these studies explored primary staging following a confirmed diagnosis of melanoma (492 participants), three evaluated re-staging of disease following some clinical indication of recurrence (589 participants), and 15 included mixed or not clearly described population groups comprising participants at a number of different points on the clinical pathway and at varying stages of disease (1265 participants). Results for whole body imaging could not be translated to a hypothetical cohort of people due to paucity of data.Most of the studies (6/9) of primary disease or re-staging of disease considered PET-CT, two in comparison to CT alone, and three studies examined the use of ultrasound. No eligible evaluations of MRI in these groups were identified. All studies used histological reference standards combined with follow-up, and two included FNAC for some participants. Observed accuracy for detection of any metastases for PET-CT was higher for re-staging of disease (summary sensitivity from two studies: 92.6%, 95% CI 85.3% to 96.4%; specificity: 89.7%, 95% CI 78.8% to 95.3%; 153 participants; 95 cases) compared to primary staging (sensitivities from individual studies ranged from 30% to 47% and specificities from 73% to 88%), and was more sensitive than CT alone in both population groups, but participant numbers were very small.No conclusions can be drawn regarding routine imaging of the brain via MRI or CT.

AUTHORS' CONCLUSIONS

Review authors found a disappointing lack of evidence on the accuracy of imaging in people with a diagnosis of melanoma at different points on the clinical pathway. Studies were small and often reported data according to the number of lesions rather than the number of study participants. Imaging with ultrasound combined with FNAC before SLNB may identify around one-fifth of those with nodal disease, but confidence intervals are wide and further work is needed to establish cost-effectiveness. Much of the evidence for whole body imaging for primary staging or re-staging of disease is focused on PET-CT, and comparative data with CT or MRI are lacking. Future studies should go beyond diagnostic accuracy and consider the effects of different imaging tests on disease management. The increasing availability of adjuvant therapies for people with melanoma at high risk of disease spread at presentation will have a considerable impact on imaging services, yet evidence for the relative diagnostic accuracy of available tests is limited.

A Learning Health Care System Using Computer-Aided Diagnosis

Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

Diagnostic Challenges of Kidney Cancer: A Systematic Review of the Role of Positron Emission Tomography-Computerized Tomography

PURPOSE

Positron emission tomography-computerized tomography is a leading imaging modality for many types of solid tumors. The ability to characterize molecular processes noninvasively during a relatively fast whole-body scan is the major advantage of this technology. We reviewed the literature in an attempt to clarify the usefulness of positron emission tomography-computerized tomography in patients with a renal mass.

MATERIALS AND METHODS

We searched PubMed® for articles published from 2004 through September 2015 using the keywords "renal," "kidney," "mass," "tumor," "cancer," and "PET/CT."

RESULTS

A total of 158 relevant articles were included in the review. Most diagnostic studies used (18)F-fluorodeoxyglucose, a marker of glucose metabolism, as the radiotracer. The results were substandard, with sensitivity rates in the range of 31.5% to 77% for diagnosis of renal cell carcinomas. There were higher success rates for diagnosis of clear cell carcinomas. Carbonic anhydrase IX is an enzyme expressed in 95% of clear cell carcinomas but not in normal renal tissue or in benign or nonclear cell malignancies. A chimeric mouse-human antibody to carbonic anhydrase IX labeled with (124)I-girentuximab was demonstrated to diagnose clear cell tumors with sensitivity of 86.2% and specificity of 85.9%. For diagnosis of metastases positron emission tomography-computerized tomography with (18)F-fluorodeoxyglucose was observed to be more accurate than computerized tomography alone (94% vs 89%). Studies with other tracers also reveal encouraging results. Positron emission tomography-computerized tomography holds great promise in predicting prognosis and response to tyrosine kinase inhibitors. Current tyrosine kinase inhibitor treatments usually induce only mild lesion shrinkage. Thus, assessment of response based on changes in size of metastases is insufficient. Low (18)F-fluorodeoxyglucose uptake before treatment and decreased uptake after 2 cycles of treatment are associated with better survival. Using labeled medications as radiotracers before actual treatment may assist in selection of the most effective medication for a specific patient.

CONCLUSIONS

Positron emission tomography-computerized tomography with (18)F-fluorodeoxyglucose currently has lower sensitivity compared to enhanced computerized tomography for diagnosis of primary renal masses but better sensitivity for diagnosis of metastases. Predicting and monitoring response to targeted therapy could direct the clinician toward drug selection or modification during therapy. The possibility of treating patients with advanced renal cell carcinoma with (124)I-girentuximab attached to (177)Lu, a strong β-emitter, is investigated.

Quantitative imaging: quantification of liver shape on CT using the statistical shape model to evaluate hepatic fibrosis

RATIONALE AND OBJECTIVES

To investigate the usefulness of the statistical shape model (SSM) for the quantification of liver shape to evaluate hepatic fibrosis.

MATERIALS AND METHODS

Ninety-one subjects (45 men and 46 women; age range, 20-75 years) were included in this retrospective study: 54 potential liver donors and 37 patients with chronic liver disease. The subjects were classified histopathologically according to the fibrosis stage as follows: F0 (n = 55); F1 (n = 6); F2 (3); F3 (n = 1); and F4 (n = 26). Each subject underwent contrast-enhanced computed tomography (CT) using a 64-channel scanner (0.625-mm slice thickness). An abdominal radiologist manually traced the liver boundaries on every CT section using an image workstation; the boundaries were used for subsequent analyses. An SSM was constructed by the principal component analysis of the subject data set, which defined a parametric model of the liver shapes. The shape parameters were calculated by fitting SSM to the segmented liver shape of each subject and were used for the training of a linear support vector regression (SVR), which classifies the liver fibrosis stage to maximize the area under the receiver operating characteristic curve (AUC). SSM/SVR models were constructed and were validated in a leave-one-out manner. The performance of our technique was compared to those of two previously reported types of caudate-right lobe ratios (C/RL-m and C/RL-r).

RESULTS

In our SSM/SVR models, the AUC values for the classification of liver fibrosis were 0.96 (F0 vs. F1-4), 0.95 (F0-1 vs. F2-4), 0.96 (F0-2 vs. F3-4), and 0.95 (F0-3 vs. F4). These values were significantly superior to AUC values using the C/RL-m or C/RL-r ratios (P < .005).

CONCLUSIONS

SSM was useful for estimating the stage of hepatic fibrosis by quantifying liver shape.

Multicentre evaluation of the INTERPRET decision support system 2.0 for brain tumour classification

In a previous study, we have shown the added value of (1) H MRS for the neuroradiological characterisation of adult human brain tumours. In that study, several methods of MRS analysis were used, and a software program, the International Network for Pattern Recognition of Tumours Using Magnetic Resonance Decision Support System 1.0 (INTERPRET DSS 1.0), with a short-TE classifier, provided the best results. Since then, the DSS evolved into a version 2.0 that contains an additional long-TE classifier. This study has two objectives. First, to determine whether clinicians with no experience of spectroscopy are comparable with spectroscopists in the use of the system, when only minimum training in the use of the system was given. Second, to assess whether or not a version with another TE is better than the initial version. We undertook a second study with the same cases and nine evaluators to assess whether the diagnostic accuracy of DSS 2.0 was comparable with the values obtained with DSS 1.0. In the second study, the analysis protocol was flexible in comparison with the first one to mimic a clinical environment. In the present study, on average, each case required 5.4 min by neuroradiologists and 9 min by spectroscopists for evaluation. Most classes and superclasses of tumours gave the same results as with DSS 1.0, except for astrocytomas of World Health Organization (WHO) grade III, in which performance measured as the area under the curve (AUC) decreased: AUC = 0.87 (0.72-1.02) with DSS 1.0 and AUC = 0.62 (0.55-0.70) with DSS 2.0. When analysing the performance of radiologists and spectroscopists with respect to DSS 1.0, the results were the same for most classes. Having data with two TEs instead of one did not affect the results of the evaluation.

Computer-assisted detection of systolic murmurs associated with hypertrophic cardiomyopathy: a pilot study

A pilot study was conducted to ascertain the level of agreement between auscultatory findings derived from heart sound recordings by a cardiologist and the results of a computer-based heart sound analysis algorithm. Heart sound recordings were obtained from volunteer subjects previously diagnosed with hypertrophic cardiomyopathy. Twenty-second recordings were obtained at each of 4 standard auscultatory locations on the precordium in 2 postures: standing and reclining. Detailed auscultatory findings were derived by a cardiologist, who listened to the heart sound recordings and was blinded to the study design. The recordings were analyzed by an algorithm that detects heart sounds and murmurs, and derives associated timing and energy parameters. The algorithm results were compared with the auscultatory findings provided by the cardiologist and correlated with the medical histories provided by the volunteer subjects. A high degree of concordance between the medical histories, auscultatory findings, and computer analyses was obtained. The 1st and 2nd heart sounds were detected with high sensitivity (90.7%) and positive predictivity (93.0%). Systolic murmurs were detected with a sensitivity that increased from 50% for grade 1 to 100% for grades 2-3 and 3. The signal energy in the mid-frequency range correlated well with murmur grade judgments, and also agreed well with the cardiologist's judgments of the relative loudness of murmurs in standing versus reclining postures. The computer analysis algorithm thus instantiates the objective detection and identification of apical systolic murmurs that are louder in standing than in reclining postures; such murmurs are a cardinal sign of hypertrophic obstructive cardiomyopathy.