Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease

INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.MATERIAL AND METHODS: An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance.RESULTS: Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found.CONCLUSIONS: The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.</p

Point-of-care therapeutic drug monitoring of tumour necrosis factor-α inhibitors using a single step immunoassay

Therapeutic drug monitoring (TDM) of tumor necrosis factor-α (TNFα)-inhibitors adalimumab and infliximab is important to establish optimal drug dose and maximize treatment efficacy. Currently, TDM is primarily performed with ELISA techniques in clinical laboratories, resulting in a long sample-to-result workflow. Point-of-care (POC) detection of these therapeutic antibodies could significantly decrease turnaround times and allow for user-friendly home-testing. Here, we adapted the recently developed bioluminescent dRAPPID (dimeric Ratiometric Plug-and-Play Immunodiagnostics) sensor platform to allow POC TDM of infliximab and adalimumab. We applied the two best performing dRAPPID sensors, with limit-of-detections of 1 pM and 17 pM, to measure the infliximab and adalimumab levels in 49 and 40 patient serum samples, respectively. The analytical performance of dRAPPID was benchmarked with commercial ELISAs and yielded Pearson's correlation coefficients of 0.93 and 0.94 for infliximab and adalimumab, respectively. Furthermore, a dedicated bioluminescence reader was fabricated and used as a readout device for the TDM dRAPPID sensors. Subsequently, infliximab and adalimumab patient serum samples were measured with the TDM dRAPPID sensors and bioluminescence reader, yielding Pearson's correlation coefficients of 0.97 and 0.86 for infliximab and adalimumab, respectively, and small proportional differences with ELISA (slope was 0.97 ± 0.09 and 0.96 ± 0.20, respectively). The adalimumab and infliximab dRAPPID sensors, in combination with the dedicated bioluminescence reader, allow for ease-of-use TDM with a fast turnaround time and show potential for POC TDM outside of clinical laboratories.</p

Point-of-care therapeutic drug monitoring of tumour necrosis factor-α inhibitors using a single step immunoassay

Therapeutic drug monitoring (TDM) of tumor necrosis factor-α (TNFα)-inhibitors adalimumab and infliximab is important to establish optimal drug dose and maximize treatment efficacy. Currently, TDM is primarily performed with ELISA techniques in clinical laboratories, resulting in a long sample-to-result workflow. Point-of-care (POC) detection of these therapeutic antibodies could significantly decrease turnaround times and allow for user-friendly home-testing. Here, we adapted the recently developed bioluminescent dRAPPID (dimeric Ratiometric Plug-and-Play Immunodiagnostics) sensor platform to allow POC TDM of infliximab and adalimumab. We applied the two best performing dRAPPID sensors, with limit-of-detections of 1 pM and 17 pM, to measure the infliximab and adalimumab levels in 49 and 40 patient serum samples, respectively. The analytical performance of dRAPPID was benchmarked with commercial ELISAs and yielded Pearson's correlation coefficients of 0.93 and 0.94 for infliximab and adalimumab, respectively. Furthermore, a dedicated bioluminescence reader was fabricated and used as a readout device for the TDM dRAPPID sensors. Subsequently, infliximab and adalimumab patient serum samples were measured with the TDM dRAPPID sensors and bioluminescence reader, yielding Pearson's correlation coefficients of 0.97 and 0.86 for infliximab and adalimumab, respectively, and small proportional differences with ELISA (slope was 0.97 ± 0.09 and 0.96 ± 0.20, respectively). The adalimumab and infliximab dRAPPID sensors, in combination with the dedicated bioluminescence reader, allow for ease-of-use TDM with a fast turnaround time and show potential for POC TDM outside of clinical laboratories.</p

Artificial intelligence based cardiotocogram assessment during labor

OBJECTIVE: To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events.STUDY DESIGN: A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation.RESULTS: The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations.CONCLUSION: The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice.</p

Contrastive Predictive Coding for Anomaly Detection of Fetal Health from the Cardiotocogram

Fetal well-being during labor is currently assessed by medical professionals through visual interpretation of the cardiotocogram (CTG), a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). This method is disputed due to high inter- and intra-observer variability and a resulting increase in the number of unnecessary interventions. A method for computerized interpretation of the CTG, based on Contrastive Predictive Coding (CPC) is presented here. We hypothesize that the CPC framework, when trained on healthy fetuses only, can predict the FHR response of healthy fetuses to UC, but will provide significant prediction error in case of fetuses with compromised condition. To that end, we have extended the original CPC model by making stochastic, recurrent, and conditioned (upon Uterine Contractions) predictions. We, moreover, introduce a new training objective that was found more suitable for the task of anomaly detection. Based on the detection of out-of-distribution behaviour and deviations from subject-specific behaviour, the proposed model is capable of achieving promising results for identification of suspicious and anomalous FHR events in the CTG, with an average correlation coefficient of 0.80±0.13 with respect to expert annotations

DLG4-related synaptopathy: a new rare brain disorder

Purpose: Postsynaptic density protein-95 (PSD-95), encoded by DLG4, regulates excitatory synaptic function in the brain. Here we present the clinical and genetic features of 53 patients (42 previously unpublished) with DLG4 variants. Methods: The clinical and genetic information were collected through GeneMatcher collaboration. All the individuals were investigated by local clinicians and the gene variants were identified by clinical exome/genome sequencing. Results: The clinical picture was predominated by early onset global developmental delay, intellectual disability, autism spectrum disorder, and attention deficit–hyperactivity disorder, all of which point to a brain disorder. Marfanoid habitus, which was previously suggested to be a characteristic feature of DLG4-related phenotypes, was found in only nine individuals and despite some overlapping features, a distinct facial dysmorphism could not be established. Of the 45 different DLG4 variants, 39 were predicted to lead to loss of protein function and the majority occurred de novo (four with unknown origin). The six missense variants identified were suggested to lead to structural or functional changes by protein modeling studies. Conclusion: The present study shows that clinical manifestations associated with DLG4 overlap with those found in other neurodevelopmental disorders of synaptic dysfunction; thus, we designate this group of disorders as DLG4-related synaptopathy. [Figure not available: see fulltext.

DLG4-related synaptopathy: a new rare brain disorder.

PURPOSE: Postsynaptic density protein-95 (PSD-95), encoded by DLG4, regulates excitatory synaptic function in the brain. Here we present the clinical and genetic features of 53 patients (42 previously unpublished) with DLG4 variants. METHODS: The clinical and genetic information were collected through GeneMatcher collaboration. All the individuals were investigated by local clinicians and the gene variants were identified by clinical exome/genome sequencing. RESULTS: The clinical picture was predominated by early onset global developmental delay, intellectual disability, autism spectrum disorder, and attention deficit-hyperactivity disorder, all of which point to a brain disorder. Marfanoid habitus, which was previously suggested to be a characteristic feature of DLG4-related phenotypes, was found in only nine individuals and despite some overlapping features, a distinct facial dysmorphism could not be established. Of the 45 different DLG4 variants, 39 were predicted to lead to loss of protein function and the majority occurred de novo (four with unknown origin). The six missense variants identified were suggested to lead to structural or functional changes by protein modeling studies. CONCLUSION: The present study shows that clinical manifestations associated with DLG4 overlap with those found in other neurodevelopmental disorders of synaptic dysfunction; thus, we designate this group of disorders as DLG4-related synaptopathy.RD&E staff can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted.Accepted version (6 month embargo), submitted versio

DLG4-related synaptopathy: a new rare brain disorder.

PURPOSE: Postsynaptic density protein-95 (PSD-95), encoded by DLG4, regulates excitatory synaptic function in the brain. Here we present the clinical and genetic features of 53 patients (42 previously unpublished) with DLG4 variants. METHODS: The clinical and genetic information were collected through GeneMatcher collaboration. All the individuals were investigated by local clinicians and the gene variants were identified by clinical exome/genome sequencing. RESULTS: The clinical picture was predominated by early onset global developmental delay, intellectual disability, autism spectrum disorder, and attention deficit-hyperactivity disorder, all of which point to a brain disorder. Marfanoid habitus, which was previously suggested to be a characteristic feature of DLG4-related phenotypes, was found in only nine individuals and despite some overlapping features, a distinct facial dysmorphism could not be established. Of the 45 different DLG4 variants, 39 were predicted to lead to loss of protein function and the majority occurred de novo (four with unknown origin). The six missense variants identified were suggested to lead to structural or functional changes by protein modeling studies. CONCLUSION: The present study shows that clinical manifestations associated with DLG4 overlap with those found in other neurodevelopmental disorders of synaptic dysfunction; thus, we designate this group of disorders as DLG4-related synaptopathy.RD&E staff can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted.Accepted version (6 month embargo), submitted versio