Machine learning in clinical practice: prospects and pitfalls

Quantifying Typical Progression of Adolescent Idiopathic Scoliosis: Longitudinal Three-Dimensional MRI Measures of Disk and Vertebral Deformities

STUDY DESIGN

A prospective cohort study.

OBJECTIVE

Detail typical three-dimensional segmental deformities and their rates of change that occur within developing adolescent idiopathic scoliosis (AIS) spines over multiple timepoints.

SUMMARY OF BACKGROUND DATA

AIS is a potentially progressive deforming condition that occurs in three dimensions of the scoliotic spine during periods of growth. However, there remains a gap for multiple timepoint segmental deformity analysis in AIS cohorts during development.

MATERIALS AND METHODS

Thirty-six female patients with Lenke 1 AIS curves underwent two to six sequential magnetic resonance images. Scans were reformatted to produce images in orthogonal dimensions. Wedging angles and rotatory values were measured for segmental elements within the major curve. Two-tailed, paired t tests compared morphologic differences between sequential scans. Rates of change were calculated for variables given the actual time between successive scans. Pearson correlation coefficients were determined for multidimensional deformity measurements.

RESULTS

Vertebral bodies were typically coronally convexly wedged, locally lordotic, convexly axially rotated, and demonstrated evidence of local mechanical torsion. Between the first and final scans, apical measures of coronal wedging and axial rotation were all greater in both vertebral and intervertebral disk morphology than nonapical regions (all reaching differences where P <0.05). No measures of sagittal deformity demonstrated a statistically significant change between scans. Cross-planar correlations were predominantly apparent between coronal and axial planes, with sagittal plane parameters rarely correlating across dimensions. Rates of segmental deformity changes between earlier scans were characterized by coronal plane convex wedging and convexly directed axial rotation. The major locally lordotic deformity changes that did occur in the sagittal plane were static between scans.

CONCLUSIONS

This novel investigation documented a three-dimensional characterization of segmental elements of the growing AIS spine and reported these changes across multiple timepoints. Segmental elements are typically deformed from initial presentation, and subsequent changes occur in separate orthogonal planes at unique times.

Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review

There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.

Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare

Healthcare has well-known challenges with safety, quality, and effectiveness, and many see artificial intelligence (AI) as essential to any solution. Emerging applications include the automated synthesis of best-practice research evidence including systematic reviews, which would ultimately see all clinical trial data published in a computational form for immediate synthesis. Digital scribes embed themselves in the process of care to detect, record, and summarize events and conversations for the electronic record. However, three persistent translational challenges must be addressed before AI is widely deployed. First, little effort is spent replicating AI trials, exposing patients to risks of methodological error and biases. Next, there is little reporting of patient harms from trials. Finally, AI built using machine learning may perform less effectively in different clinical settings.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery

In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g. diagnostic accuracy) as well as reduce the burden on clinicians (e.g. documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery

Background

Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS.

Methods

The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters.

Results

A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97–4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9–8.5)].

Conclusion

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.

Identification of a 12-Gene Signature and Hub Genes Involved in Kidney Wilms Tumor via Integrated Bioinformatics Analysis

Wilms tumor (WT), also known as nephroblastoma, is a rare primary malignancy in all kinds of tumor. With the development of second-generation sequencing, the discovery of new tumor markers and potential therapeutic targets has become easier. This study aimed to explore new WT prognostic biomarkers. In this study, WT-miRNA datasets GSE57370 and GSE73209 were selected for expression profiling to identify differentially expressed genes. The key gene miRNA, namely hsa-miR-30c-5p, was identified by overlapping, and the target gene of candidate hsa-miR-30c-5p was predicted using an online database. Furthermore, 384 genes were obtained by intersecting them with differentially expressed genes in the TARGET-WT database, and the genes were analyzed for pathway and functional enrichment. Kaplan–Meier survival analysis of the 384 genes yielded a total of 25 key genes associated with WT prognosis. Subsequently, a prediction model with 12 gene signatures (BCL6, CCNA1, CTHRC1, DGKD, EPB41L4B, ERRFI1, LRRC40, NCEH1, NEBL, PDSS1, ROR1, and RTKN2) was developed. The model had good predictive power for the WT prognosis at 1, 3, and 5 years (AUC: 0.684, 0.762, and 0.774). Finally, ERRFI1 (hazard ratios [HR] = 1.858, 95% confidence intervals [CI]: 1.298–2.660) and ROR1 (HR = 0.780, 95% CI: 0.609–0.998) were obtained as independent predictors of prognosis in WT patients by single, multifactorial Cox analysis.

Maslow's hammer: considering the perils of solutionism in mental healthcare and psychiatric practice

OBJECTIVE

To discuss narrow pragmatism, manifest as digital and technical solutionism, in mental healthcare and psychiatric practice. Pragmatism is a view of the field of psychiatry as an instrument or tool for the purpose of providing psychiatric care for people with a mental illness. Solutionism, as proposed by Morozov, can be considered a special case of pragmatism that valorises an approach to solving real-world problems based on computation, algorithms and digital technology,¹ which we extend to discuss other technical solutions such as medication, non-invasive brain stimulation and psychotherapy.

CONCLUSIONS

Digital or technical solutionism may unnecessarily constrain approaches to mental healthcare and psychiatric practice. Psychiatrists can consider, and should advocate for, appropriate adaptation of technology and technical solutions toward collaborative and effective mental healthcare.

Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases

There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.

What is needed to mainstream artificial intelligence in health care?

Artificial intelligence (AI) has become a mainstream technology in many industries, but not yet in health care. Although basic research and commercial investment are burgeoning across various clinical disciplines, AI remains relatively non-existent in most healthcare organisations. This is despite hundreds of AI applications having passed proof-of-concept phase, and scores receiving regulatory approval overseas. AI has considerable potential to optimise multiple care processes, maximise workforce capacity, reduce waste and costs, and improve patient outcomes. The current obstacles to wider AI adoption in health care and the pre-requisites for its successful development, evaluation and implementation need to be defined.

Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

Artificial intelligence (AI), particularly the deep learning technology, have been proven influential to radiology in the recent decade. Its ability in image classification, segmentation, detection and reconstruction tasks have substantially assisted diagnostic radiology, and has even been viewed as having the potential to perform better than radiologists in some tasks. Gastrointestinal radiology, an important subspecialty dealing with complex anatomy and various modalities including endoscopy, have especially attracted the attention of AI researchers and engineers worldwide. Consequently, recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology, particularly in the fields for which public databases are available, such as diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology, with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT. For fields where AI is less developed, this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues. The authors’ insights of possible future development will be addressed in the last section.

Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

BACKGROUND

Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.

METHODS

In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques.

RESULTS

Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years.

CONCLUSION

In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables.

TRIAL REGISTRATION

Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation.

Using Artificial Intelligence in Infection Prevention

Purpose of Review

Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene.

Recent Findings

AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed.

Summary

Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.