Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly

Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly

OBJECTIVES

To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs.

METHODS

Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA.

CONCLUSIONS

Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs.

CLINICAL RELEVANCE STATEMENT

It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations.

KEY POINTS

Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Crossing the AI Chasm in Neurocritical Care

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.

Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy

STUDY OBJECTIVE

The pharmacokinetics and pharmacodynamics of tacrolimus (TAC) vary greatly among individuals, hindering its precise utilization. Moreover, effective models for the early prediction of TAC efficacy in patients with nephrotic syndrome (NS) are lacking. We aimed to identify key factors affecting TAC efficacy and develop efficacy prediction models for childhood NS using machine learning algorithms.

DESIGN

This was an observational cohort study of patients with pediatric refractory NS.

SETTING

Guangzhou Women and Children's Medical Center between June 2013 and December 2018.

PATIENTS

203 patients with pediatric refractory NS were used for model generation and 35 patients were used for model validation.

INTERVENTION

All patients regularly received double immunosuppressive therapy comprising TAC and low-dose prednisone or methylprednisolone. In this observational cohort study of 203 pediatric patients with refractory NS, clinical and genetic variables, including single-nucleotide polymorphism (SNPs), were identified. TAC efficacy was evaluated 3 months after administration according to two different evaluation criteria: response or non-response (Group 1) and complete remission, partial remission, or non-remission (Group 2).

MEASUREMENTS

Logistic regression, extremely random trees, gradient boosting decision trees, random forest, and extreme gradient boosting algorithms were used to develop and validate the models. Prediction models were validated among a cohort of 35 patients with NS.

MAIN RESULTS

The random forest models performed best in both groups, and the area under the receiver operating characteristics curve of these two models was 80.7% (Group 1) and 80.3% (Group 2). These prediction models included urine erythrocyte count before administration, steroid types, and eight SNPs (ITGB4 rs2290460, TRPC6 rs3824934, CTGF rs9399005, IL13 rs20541, NFKBIA rs8904, NFKBIA rs8016947, MAP3K11 rs7946115, and SMARCAL1 rs11886806).

CONCLUSIONS

Two pre-administration models with good predictive performance for TAC response of patients with NS were developed and validated using machine learning algorithms. These accurate models could assist clinicians in predicting TAC efficacy in pediatric patients with NS before utilization to avoid treatment failure or adverse effects.

Applications of artificial intelligence in obstetrics

Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.

The current and future roles of artificial intelligence in pediatric radiology

Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.

Incidence, follow-up, and postnatal clinical progress of children with central nervous system anomalies on fetal MRI

OBJECTIVE

MRI is increasingly employed to assess intrauterine fetal anomalies. Central nervous system (CNS) anomalies are common structural conditions that warrant evaluation with fetal MRI and subsequent prenatal consultation with a pediatric neurosurgeon. As the use of fetal MRI increases, there is greater impetus to understand the most common CNS structural anomalies diagnosed in utero, as well as their natural histories.

METHODS

The authors performed a single-center retrospective review of fetal MRI evaluations performed between January 2012 and December 2020. Children who underwent both prenatal and postnatal neurosurgical evaluations of CNS anomalies were included. Specific CNS anomalies on fetal MRI, associated extra-CNS findings, and suspicion for genetic abnormality or syndromes were noted. Postnatal clinical status and interventions were assessed.

RESULTS

Between January 2012 and December 2020, a total of 469 fetal MRI evaluations were performed; of these, 114 maternal-fetal pairs had CNS anomalies that warranted prenatal consultation and postnatal pediatric neurosurgical follow-up. This cohort included 67 male infants (59%), with a mean ± SD follow-up of 29.8 ± 25.0 months after birth. Fetal MRI was performed at 27.3 ± 5.8 weeks of gestational age. The most frequently reported CNS abnormalities were ventriculomegaly (57%), agenesis or thinning of the corpus callosum (33%), Dandy-Walker complex (DWC) (21%), neuronal migration disorders (18%), and abnormalities of the septum pellucidum (17%). Twenty-one children (18%) required neurosurgical intervention at a mean age of 2.4 ± 3.7 months. The most common surgical conditions included myelomeningocele, moderate to severe ventriculomegaly, encephalocele, and arachnoid cyst. Corpus callosum agenesis or thinning was associated with developmental delay (p = 0.02) and systemic anomalies (p = 0.05). The majority of prenatal patients referred for DWC had Dandy-Walker variants that did not require surgical intervention.

CONCLUSIONS

The most common conditions for prenatal neurosurgical assessment were ventriculomegaly, corpus callosum anomaly, and DWC, whereas the most common surgical conditions were myelomeningocele, hydrocephalus, and arachnoid cyst. Only 18% of prenatal neurosurgical consultations resulted in surgical intervention during infancy. The majority of referrals for prenatal mild ventriculomegaly and DWC were not associated with developmental or surgical sequelae. Patients with corpus callosum abnormalities should be concurrently referred to a neurologist for developmental assessments.

Artificial intelligence applied to fetal MRI: A scoping review of current research

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network

PURPOSE

While conventional statistical approaches have been used to identify risk factors for cerebrospinal fluid (CSF) shunt failure, these methods may not fully capture the complex contribution of clinical, radiologic, surgical, and shunt-specific variables influencing this outcome. Using prospectively collected data from the Hydrocephalus Clinical Research Network (HCRN) patient registry, we applied machine learning (ML) approaches to create a predictive model of CSF shunt failure.

METHODS

Pediatric patients (age < 19 years) undergoing first-time CSF shunt placement at six HCRN centers were included. CSF shunt failure was defined as a composite outcome including requirement for shunt revision, endoscopic third ventriculostomy, or shunt infection within 5 years of initial surgery. Performance of conventional statistical and 4 ML models were compared.

RESULTS

Our cohort consisted of 1036 children undergoing CSF shunt placement, of whom 344 (33.2%) experienced shunt failure. Thirty-eight clinical, radiologic, surgical, and shunt-design variables were included in the ML analyses. Of all ML algorithms tested, the artificial neural network (ANN) had the strongest performance with an area under the receiver operator curve (AUC) of 0.71. The ANN had a specificity of 90% and a sensitivity of 68%, meaning that the ANN can effectively rule-in patients most likely to experience CSF shunt failure (i.e., high specificity) and moderately effective as a tool to rule-out patients at high risk of CSF shunt failure (i.e., moderately sensitive). The ANN was independently validated in 155 patients (prospectively collected, retrospectively analyzed).

CONCLUSION

These data suggest that the ANN, or future iterations thereof, can provide an evidence-based tool to assist in prognostication and patient-counseling immediately after CSF shunt placement.

Prognostic Analysis and Risk Factors Associated with Fetal Ventriculomegaly

BACKGROUND

This study aimed to investigate the clinical outcome and related risk factors of fetal lateral ventriculomegaly (VM).

METHODS

A retrospective analysis was performed on 255 cases diagnosed as fetal VM. Prenatal imaging examination was carried out. The pregnancy outcomes were investigated through follow-up. According to the prognosis of children, they were divided into case group and control group. Multivariate logistic regression was used to analyze the factors influencing the prognosis of hydrocephalus.

RESULTS

After excluding the cases with either loss of follow-up or incomplete information, 102 cases were followed up. Twelve cases with poor prognosis were set as the case group. According to the maternal age, gestational age, gender of children, and follow-up time, 3 cases were selected from the other 90 cases for each child in the case group, respectively, and selected as the control group. Paired comparative analysis was performed on 48 cases. Using prognosis as a dependent variable, multivariate logistic regression analysis of the statistically significant factors indicated that the change speed of width ratio (CSWR) and maximum lateral ventricular width (MW) were associated with fetal prognosis.

CONCLUSIONS

Our results suggested that CSWR and MW may have the value of predicting fetal prognosis.

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.

Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty

MRI-based brain age prediction has been widely used to characterize normal brain development, and deviations from the typical developmental trajectory are indications of brain abnormalities. Age prediction of the fetal brain remains unexplored, although it can be of broad interest to prenatal examination given the limited diagnostic tools available for assessment of the fetal brain. In this work, we built an attention-based deep residual network based on routine clinical T2-weighted MR images of 659 fetal brains, which achieved an overall mean absolute error of 0.767 weeks and R² of 0.920 in fetal brain age prediction. The predictive uncertainty and estimation confidence were simultaneously quantified from the network as markers for detecting fetal brain anomalies using an ensemble method. The novel markers overcame the limitations in conventional brain age estimation and demonstrated promising diagnostic power in differentiating several types of fetal abnormalities, including small head circumference, malformations and ventriculomegaly with the area under the curve of 0.90, 0.90 and 0.67, respectively. In addition, attention maps were derived from the network, which revealed regional features that contributed to fetal age estimation at each gestational stage. The proposed attention-based deep ensembles demonstrated superior performance in fetal brain age estimation and fetal anomaly detection, which has the potential to be translated to prenatal diagnosis in clinical practice.