Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia-is preventive and personalized approach on the horizon?

Using generative AI to investigate medical imagery models and datasets

BACKGROUND

AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts.

METHODS

In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries).

FINDINGS

To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown).

INTERPRETATION

Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes.

FUNDING

Google.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Artificial intelligence for the vascular surgeon

In recent years, artificial intelligence (AI) has permeated different aspects of vascular surgery to solve challenges in clinical practice. Although AI in vascular surgery is still in its early stages, there have been promising developments in its applications to vascular diagnosis, risk stratification, and outcome prediction. By establishing a baseline knowledge of AI, vascular surgeons are better equipped to use and interpret the data from these types of projects. This review aims to provide an overview of the fundamentals of AI and highlight its role in helping vascular surgeons overcome the challenges of clinical practice. In addition, we discuss the limitations of AI and how they affect AI applications.

Applications of artificial intelligence for patients with peripheral artery disease

OBJECTIVE

Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field.

METHODS

We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML).

RESULTS

NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow.

CONCLUSIONS

AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.

Frailty as a Superior Predictor of Dysphagia and Surgically Placed Feeding Tube Requirement After Anterior Cervical Discectomy and Fusion Relative to Age

Frailty is a measure of physiological reserve that has been demonstrated to be a discriminative predictor of worse outcomes across multiple surgical subspecialties. Anterior cervical discectomy and fusion (ACDF) is one of the most common neurosurgical procedures in the United States and has a high incidence of postoperative dysphagia. To determine the association between frailty and dysphagia after ACDF and compare the predictive value of frailty and age. 155,300 patients with cervical stenosis (CS) who received ACDF were selected from the 2016-2019 National Inpatient Sample (NIS) utilizing International Classification of Disease, tenth edition (ICD-10) codes. The 11-point modified frailty index (mFI-11) was used to stratify patients based on frailty: mFI-11 = 0 was robust, mFI-11 = 1 was prefrail, mFI-11 = 2 was frail, and mFI-11 = 3 + was characterized as severely frail. Demographics, complications, and outcomes were compared between frailty groups. A total of 155,300 patients undergoing ACDF for CS were identified, 33,475 (21.6%) of whom were frail. Dysphagia occurred in 11,065 (7.1%) of all patients, and its incidence was significantly higher for frail patients (OR 1.569, p < 0.001). Frailty was a risk factor for postoperative complications (OR 1.681, p < 0.001). Increasing frailty and undergoing multilevel ACDF were significant independent predictors of negative postoperative outcomes, including dysphagia, surgically placed feeding tube (SPFT), prolonged LOS, non-home discharge, inpatient death, and increased total charges (p < 0.001 for all). Increasing mFI-11 score has better prognostic value than patient age in predicting postoperative dysphagia and SPFT after ACDF.

Sex Disparities in the Association of Serum Uric Acid With Kidney Stone: A Cross-Sectional Study in China

Background and Aims

Urolithiasis is characterized by high rates of prevalence and recurrence. Hyperuricemia is related to various diseases. We hope to determine the association between serum uric acid (UA) level and kidney stone (KS).

Methods

In this population-based cross-sectional study, a total of 82,017 Chinese individuals who underwent a comprehensive examination in 2017 were included. The KS was diagnosed based on ultrasonography examination outcomes. Fully adjusted odds ratio (OR) for KS, and mean difference between the two groups were applied to determine the association of UA level with KS.

Results

Among the 82,017 participants included in this study (aged 18~99 years), 9,435 participants (11.5%) are diagnosed with KS. A proportion of 56.3% of individuals is male. The mean UA level of overall participants is 341.77 μmol/L. The participants with KS report higher UA level than the participants without KS [mean UA level 369.91 vs. 338.11 μmol/L; mean difference (MD), 31.96 (95% CI, 29.61~34.28) μmol/L]. In men, the OR for KS significantly increases from 330 μmol/L UA level. Every 50 μmol/L elevation of UA level increases the risk of KS formation by about 10.7% above the UA level of 330 μmol/L in men. The subgroup analysis for male is consistent with the overall result except for the participants presenting underweight [adjusted OR, 1.035 (0.875~1.217); MD, −5.57 (−16.45~11.37)], low cholesterol [adjusted OR, 1.088 (0.938~1.261); MD, 8.18 (−7.93~24.68)] or high estimated glomerular filtration rate (eGFR) [adjusted OR, 1.044 (0.983~1.108); MD, 5.61 (−1.84~13.36)]. However, no significant association is observed in women between UA and KS either in all female participants or in female subgroups.

Conclusion

Among Chinese adults, UA level is associated with KS in a dose-response manner in men but not in women. However, the association becomes considerably weak in male participants with malnutrition status.

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.

Health screening program revealed risk factors associated with development and progression of papillomacular bundle defect

BACKGROUND/AIMS

The papillomacular bundle (PMB) area is an important anatomical site associated with central vision. As preventive medicine and health screening examinations are now becoming commonplace, the incidental detection of papillomacular bundle defect (PMBD) on fundus photography has been increasing. However, clinical significance of incidental PMBD has not been well documented to date. Thus, through long-term and longitudinal observation, we aimed to investigate the risk factors for the development and progression of PMBD and its predictive role associated with systemic diseases and glaucoma.

METHODS

This longitudinal study included subjects who had undergone standardized health screening. We retrospectively reviewed patients for whom PMBD had been detected in fundus photography and followed up for more than 5 years. For a comparative analysis, non-PMBD groups of age- and gender-matched healthy controls were selected.

RESULTS

A total of about 67,000 fundus photographs were analyzed for 8.0 years, and 587 PMBD eyes were found. Among them, 234 eyes of 234 patients who had had fundus photographs taken for more than 5 years were finally included. A total of 216 eyes (92.3%) did not progress during the 8.1 ± 2.7 years, whereas 18 eyes (7.7%) showed progression at 7.6 ± 2.9 years after initial detection. A multivariate logistic regression analysis using 224 non-PMBD healthy controls revealed low body mass index (BMI < 20 kg/m2), systemic hypertension, and sclerotic changes of retinal artery as the significant risk factors for the development of PMBD. Regarding PMBD progression, low BMI, concomitant retinal nerve fiber layer defect (RNFLD) at non-PMB sites, optic disc hemorrhage, and higher vertical cup/disc ratio were individual significant risk factors.

CONCLUSION

PMBD is associated with ischemic effects. Although the majority of PMBD do not progress, some of cases are associated with glaucomatous damage in a long-term way. PMBD might be a personalized indicator representing ischemia-associated diseases and a predictive factor for diagnosis and preventive management of glaucoma.

Endothelial progenitor cells as the target for cardiovascular disease prediction, personalized prevention, and treatments: progressing beyond the state-of-the-art

Stimulated by the leading mortalities of cardiovascular diseases (CVDs), various types of cardiovascular biomaterials have been widely investigated in the past few decades. Although great therapeutic effects can be achieved by bare metal stents (BMS) and drug-eluting stents (DES) within months or years, the long-term complications such as late thrombosis and restenosis have limited their further applications. It is well accepted that rapid endothelialization is a promising approach to eliminate these complications. Convincing evidence has shown that endothelial progenitor cells (EPCs) could be mobilized into the damaged vascular sites systemically and achieve endothelial repair in situ, which significantly contributes to the re-endothelialization process. Therefore, how to effectively capture EPCs via specific molecules immobilized on biomaterials is an important point to achieve rapid endothelialization. Further, in the context of predictive, preventive, personalized medicine (PPPM), the abnormal number alteration of EPCs in circulating blood and certain inflammation responses can also serve as important indicators for predicting and preventing early cardiovascular disease. In this contribution, we mainly focused on the following sections: the definition and classification of EPCs, the mechanisms of EPCs in treating CVDs, the potential diagnostic role of EPCs in predicting CVDs, as well as the main strategies for cardiovascular biomaterials to capture EPCs.