Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT.

Background There is clinical need to better quantify lung disease severity in pulmonary hypertension (PH), particularly in idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-LD). Purpose To quantify fibrosis on CT pulmonary angiograms using an artificial intelligence (AI) model and to assess whether this approach can be used in combination with radiologic scoring to predict survival. Materials and Methods This retrospective multicenter study included adult patients with IPAH or PH-LD who underwent incidental CT imaging between February 2007 and January 2019. Patients were divided into training and test cohorts based on the institution of imaging. The test cohort included imaging examinations performed in 37 external hospitals. Fibrosis was quantified using an established AI model and radiologically scored by radiologists. Multivariable Cox regression adjusted for age, sex, World Health Organization functional class, pulmonary vascular resistance, and diffusing capacity of the lungs for carbon monoxide was performed. The performance of predictive models with or without AI-quantified fibrosis was assessed using the concordance index (C index). Results The training and test cohorts included 275 (median age, 68 years [IQR, 60-75 years]; 128 women) and 246 (median age, 65 years [IQR, 51-72 years]; 142 women) patients, respectively. Multivariable analysis showed that AI-quantified percentage of fibrosis was associated with an increased risk of patient mortality in the training cohort (hazard ratio, 1.01 [95% CI: 1.00, 1.02]; P = .04). This finding was validated in the external test cohort (C index, 0.76). The model combining AI-quantified fibrosis and radiologic scoring showed improved performance for predicting patient mortality compared with a model including radiologic scoring alone (C index, 0.67 vs 0.61; P < .001). Conclusion Percentage of lung fibrosis quantified on CT pulmonary angiograms by an AI model was associated with increased risk of mortality and showed improved performance for predicting patient survival when used in combination with radiologic severity scoring compared with radiologic scoring alone. © RSNA, 2024 Supplemental material is available for this article.

Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension.

PURPOSE OF REVIEW: Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a central role in determining the phenotype of pulmonary hypertension, informing treatment strategies. Many artificial intelligence tools have been developed in this modality for the assessment of pulmonary hypertension. This article reviews the latest CT artificial intelligence applications in pulmonary hypertension and related diseases. RECENT FINDINGS: Multistructure segmentation tools have been developed in both pulmonary hypertension and nonpulmonary hypertension cohorts using state-of-the-art UNet architecture. These segmentations correspond well with those of trained radiologists, giving clinically valuable metrics in significantly less time. Artificial intelligence lung parenchymal assessment accurately identifies and quantifies lung disease patterns by integrating multiple radiomic techniques such as texture analysis and classification. This gives valuable information on disease burden and prognosis. There are many accurate artificial intelligence tools to detect acute pulmonary embolism. Detection of chronic pulmonary embolism proves more challenging with further research required. SUMMARY: There are numerous artificial intelligence tools being developed to identify and quantify many clinically relevant parameters in both pulmonary hypertension and related disease cohorts. These potentially provide accurate and efficient clinical information, impacting clinical decision-making.

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.

OBJECTIVES: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). METHODS: This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. RESULTS: A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. CONCLUSION: State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. CLINICAL RELEVANCE: Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. KEY POINTS: • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.

Establishing minimally important differences for cardiac MRI end-points in pulmonary arterial hypertension.

BACKGROUND: Cardiac magnetic resonance (CMR) is the gold standard technique to assess biventricular volumes and function, and is increasingly being considered as an end-point in clinical studies. Currently, with the exception of right ventricular (RV) stroke volume and RV end-diastolic volume, there is only limited data on minimally important differences (MIDs) reported for CMR metrics. Our study aimed to identify MIDs for CMR metrics based on US Food and Drug Administration recommendations for a clinical outcome measure that should reflect how a patient "feels, functions or survives". METHODS: Consecutive treatment-naïve patients with pulmonary arterial hypertension (PAH) between 2010 and 2022 who had two CMR scans (at baseline prior to treatment and 12 months following treatment) were identified from the ASPIRE registry. All patients were followed up for 1 additional year after the second scan. For both scans, cardiac measurements were obtained from a validated fully automated segmentation tool. The MID in CMR metrics was determined using two distribution-based (0.5sd and minimal detectable change) and two anchor-based (change difference and generalised linear model regression) methods benchmarked to how a patient "feels" (emPHasis-10 quality of life questionnaire), "functions" (incremental shuttle walk test) or "survives" for 1-year mortality to changes in CMR measurements. RESULTS: 254 patients with PAH were included (mean±sd age 53±16 years, 79% female and 66% categorised as intermediate risk based on the 2022 European Society of Cardiology/European Respiratory Society risk score). We identified a 5% absolute increase in RV ejection fraction and a 17 mL decrease in RV end-diastolic or end-systolic volumes as the MIDs for improvement. Conversely, a 5% decrease in RV ejection fraction and a 10 mL increase in RV volumes were associated with worsening. CONCLUSIONS: This study establishes clinically relevant CMR MIDs for how a patient "feels, functions or survives" in response to PAH treatment. These findings provide further support for the use of CMR as a clinically relevant clinical outcome measure and will aid trial size calculations for studies using CMR.

Plain language summaryPulmonary arterial hypertension (PAH) is a disease of the vessels of the lung that causes their narrowing and stiffening. As a result, the heart pumping blood into these diseased lung vessels has to work harder and eventually gets worn out. PAH can affect patients' ability to function in daily activities and impact their quality of life. It also reduces their life expectancy dramatically. Patients are, therefore, often monitored and undergo several investigations to adapt treatment according to their situation. These investigations include a survey of how a patient feels (the emPHasis-10 questionnaire), functions (walking test) and how well the heart is coping with the disease (MRI of the heart). Until now, it is unclear how changes on MRI of the heart reflect changes in how a patient feels and functions. Our study identified patients that had the emPHasis-10 questionnaire, walking test and MRI of the heart at both the time of PAH diagnosis and one year later. This allowed us to compare how the changes in the different tests relate to each other. And because previous research identified thresholds for important changes in the emPHasis-10 questionnaire and the walking tests, we were able to use these tests as a benchmark for changes in the MRI of the heart. Our study identified thresholds for change on heart MRI that might indicate whether a patient has improved or worsened. This finding might have implications for how patients are monitored in clinical practice and future research on PAH treatments.

Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction.

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of patients, including a broad spectrum of right ventricular and left ventricular conditions, with internal and external testing. Fully automatic cardiac MRI assessment correlated strongly with invasive hemodynamics, had prognostic value, were highly repeatable, and showed excellent generalizability. Clinical trial registration no. NCT03841344 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Ambale-Venkatesh and Lima in this issue. An earlier incorrect version appeared online. This article was corrected on June 27, 2022.

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.

BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. METHODS: A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). RESULTS: All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. CONCLUSION: Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.

Microdeletion of 9q22.3: A patient with minimal deletion size associated with a severe phenotype.

Basal cell nevus syndrome (also known as Gorlin Syndrome; MIM109400) is an autosomal dominant disorder characterized by recurrent pathological features such as basal cell carcinomas and odontogenic keratocysts as well as skeletal abnormalities. Most affected individuals have point mutations or small insertions or deletions within the PTCH1 gene on human chromosome 9, but there are some cases with more extensive deletion of the region, usually including the neighboring FANCC and/or ERCC6L2 genes. We report a 16-year-old patient with a deletion of approximately 400,000 bases which removes only PTCH1 and some non-coding RNA genes but leaves FANCC and ERCC6L2 intact. In spite of the small amount of DNA for which he is haploid, his phenotype is more extreme than many individuals with longer deletions in the region. This includes early presentation with a large number of basal cell nevi and other skin lesions, multiple jaw keratocysts, and macrosomia. We found that the deletion was in the paternal chromosome, in common with other macrosomia cases. Using public databases, we have examined possible interactions between sequences within and outside the deletion and speculate that a regulatory relationship exists with flanking genes, which is unbalanced by the deletion, resulting in abnormal activation or repression of the target genes and hence the severity of the phenotype.

Mitochondrial phylogenomics of the Hymenoptera.

The insect order Hymenoptera presents marvelous morphological and ecological diversity. Higher-level hymenopteran relationships remain controversial, even after recent phylogenomic analyses, as their taxon sampling was limited. To shed light on the origin and diversification of Hymenoptera, in particular the poorly studied Parasitica, we undertook phylogenetic analyses of 40 newly and 43 previously sequenced mitochondrial genomes representing all major clades of Hymenoptera. Various Bayesian inferences using different data partitions and phylogenetic methods recovered similar phylogenetic trees with strong statistical support for almost all nodes. Novel findings of the mitogenomic phylogeny mainly affected the three infraorders Ichneumonomorpha, Proctotrupomorpha and Evaniomorpha, the latter of which was split into three clades. Basal relationships of Parasitica recovered Stephanoidea + (Gasteruptiidae + Aulacidae) as the sister group to Ichneumonomorpha + (Trigonalyoidea + Megalyroidea). This entire clade is sister to Proctotrupomorpha, and Ceraphronoidea + Evaniidae is sister to Aculeata (stinging wasps). Our divergence time analysis indicates that major hymenopteran lineages originated in the Mesozoic. The radiation of early apocritans may have been triggered by the Triassic-Jurassic mass extinction; all extant families were present by the Cretaceous.

Phylogenetic analyses elucidate the inter-relationships of Pamphilioidea (Hymenoptera, Symphyta).

The phylogeny of the superfamily Pamphilioidea is reconstructed using morphology and DNA sequence data of living and fossil taxa by employing two phylogenetic methods (maximum parsimony and Bayesian inference). Based on our results, the monophyly of Pamphilioidea and Pamphiliidae are corroborated, whereas two extinct families, Xyelydidae and Praesiricidae, are not monophyletic. Because members of Praesiricidae together with Megalodontes form a monophyletic group, we propose that the paraphyletic Praesiricidae is synonymized under Megalodontesidae (syn. nov.). The origin of Pamphilioidea is hypothesized to be as early as the Early Jurassic. To better understand morphological evolution in the early lineages of Pamphilioidea, ancestral states of the first flagellomere and the first and second abdominal terga are reconstructed on the morphology-based tree. In addition, three new genera (Medilyda, Brevilyda, Strenolyda) with five new species (Medilyda procera, M. distorta, Brevilyda provecta, Strenolyda marginalis and S. retrorsa) are described based on well-preserved xyelydid fossils from the Middle Jurassic Jiulongshan Formation of north-eastern China.

Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.

Background: Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Methods: Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82-0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Conclusion: Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings. Systematic Review Registration: [www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].

A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.

Background: Chronic pulmonary embolism (PE) may result in pulmonary hypertension (CTEPH). Automated CT pulmonary angiography (CTPA) interpretation using artificial intelligence (AI) tools has the potential for improving diagnostic accuracy, reducing delays to diagnosis and yielding novel information of clinical value in CTEPH. This systematic review aimed to identify and appraise existing studies presenting AI tools for CTPA in the context of chronic PE and CTEPH. Methods: MEDLINE and EMBASE databases were searched on 11 September 2023. Journal publications presenting AI tools for CTPA in patients with chronic PE or CTEPH were eligible for inclusion. Information about model design, training and testing was extracted. Study quality was assessed using compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Five studies were eligible for inclusion, all of which presented deep learning AI models to evaluate PE. First study evaluated the lung parenchymal changes in chronic PE and two studies used an AI model to classify PE, with none directly assessing the pulmonary arteries. In addition, a separate study developed a CNN tool to distinguish chronic PE using 2D maximum intensity projection reconstructions. While another study assessed a novel automated approach to quantify hypoperfusion to help in the severity assessment of CTEPH. While descriptions of model design and training were reliable, descriptions of the datasets used in training and testing were more inconsistent. Conclusion: In contrast to AI tools for evaluation of acute PE, there has been limited investigation of AI-based approaches to characterising chronic PE and CTEPH on CTPA. Existing studies are limited by inconsistent reporting of the data used to train and test their models. This systematic review highlights an area of potential expansion for the field of AI in medical image interpretation.There is limited knowledge of A systematic review of artificial intelligence tools for chronic pulmonary embolism in CT. This systematic review provides an assessment on research that examined deep learning algorithms in detecting CTEPH on CTPA images, the number of studies assessing the utility of deep learning on CTPA in CTEPH was unclear and should be highlighted.

Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR.

Introduction: Cardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR. Methods: Consecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists. Results: 462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s. Conclusions: Automated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis.

Weighted compromise trees: a method to summarize competing phylogenetic hypotheses.

A new consensus method for summarizing competing phylogenetic hypotheses, weighted compromise, is described. The method corrects for a bias inherent in majority-rule consensus/compromise trees when the source trees exhibit non-independence due to ambiguity in terminal clades. Suggestions are given for its employment in parsimony analyses and tree resampling strategies such as bootstrapping and jackknifing. An R function is described that can be used with the programming language R to produce the consensus.

A comparison of two novel alcohol dehydrogenase enzymes (ADH1 and ADH2) from the extreme halophile Haloferax volcanii.

Haloarchaeal alcohol dehydrogenases are exciting biocatalysts with potential industrial applications. In this study, two alcohol dehydrogenase enzymes from the extremely halophilic archaeon Haloferax volcanii (HvADH1 and HvADH2) were homologously expressed and subsequently purified by immobilized metal-affinity chromatography. The proteins appeared to copurify with endogenous alcohol dehydrogenases, and a double Δadh2 Δadh1 gene deletion strain was constructed to prevent this occurrence. Purified HvADH1 and HvADH2 were compared in terms of stability and enzymatic activity over a range of pH values, salt concentrations, and temperatures. Both enzymes were haloalkaliphilic and thermoactive for the oxidative reaction and catalyzed the reductive reaction at a slightly acidic pH. While the NAD(+)-dependent HvADH1 showed a preference for short-chain alcohols and was inherently unstable, HvADH2 exhibited dual cofactor specificity, accepted a broad range of substrates, and, with respect to HvADH1, was remarkably stable. Furthermore, HvADH2 exhibited tolerance to organic solvents. HvADH2 therefore displays much greater potential as an industrially useful biocatalyst than HvADH1.

A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension.

Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model's prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network's prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches (PCA-GradCam, PCA-Shape) was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results.

Ophiclypeus, a new genus of Cardiochilinae Ashmead (Hymenoptera, Braconidae) from the Oriental region with descriptions of three new species.

A new genus of the braconid subfamily Cardiochilinae, Ophiclypeusgen. nov., is described and illustrated based on three new species: O.chiangmaiensis Kang, sp. nov. type species (type locality: Chiang Mai, Thailand), O.dvaravati Ghafouri Moghaddam, Quicke & Butcher, sp. nov. (type locality: Saraburi, Thailand), and O.junyani Kang, sp. nov. (type locality: Dalin, Taiwan). We provide morphological diagnostic characters to separate the new genus from other cardiochiline genera. A modified key couplet (couplet 5) and a new key couplet (couplet 16) are provided with detailed images for Dangerfield's key to the world cardiochiline genera to facilitate recognition of Ophiclypeusgen. nov.

Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population.

OBJECTIVES: Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs. DESIGN: This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs. PARTICIPANTS: 5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer). RESULTS: Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases. CONCLUSIONS: The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.

Crystal structure of NAD+-dependent Peptoniphilus asaccharolyticus glutamate dehydrogenase reveals determinants of cofactor specificity.

Glutamate dehydrogenases (EC 1.4.1.2-4) catalyse the oxidative deamination of l-glutamate to α-ketoglutarate using NAD(P) as a cofactor. The bacterial enzymes are hexamers and each polypeptide consists of an N-terminal substrate-binding (Domain I) followed by a C-terminal cofactor-binding segment (Domain II). The reaction takes place at the junction of the two domains, which move as rigid bodies and are presumed to narrow the cleft during catalysis. Distinct signature sequences in the nucleotide-binding domain have been linked to NAD(+) vs. NADP(+) specificity, but they are not unambiguous predictors of cofactor preferences. Here, we have determined the crystal structure of NAD(+)-specific Peptoniphilus asaccharolyticus glutamate dehydrogenase in the apo state. The poor quality of native crystals was resolved by derivatization with selenomethionine, and the structure was solved by single-wavelength anomalous diffraction methods. The structure reveals an open catalytic cleft in the absence of substrate and cofactor. Modeling of NAD(+) in Domain II suggests that a hydrophobic pocket and polar residues contribute to nucleotide specificity. Mutagenesis and isothermal titration calorimetry studies of a critical glutamate at the P7 position of the core fingerprint confirms its role in NAD(+) binding. Finally, the cofactor binding site is compared with bacterial and mammalian enzymes to understand how the amino acid sequences and three-dimensional structures may distinguish between NAD(+) vs. NADP(+) recognition.

Species identification in the taxonomically neglected, highly diverse, neotropical parasitoid wasp genus Notiospathius (Braconidae: Doryctinae) based on an integrative molecular and morphological approach.

Various DNA sequence-based methods for species delineation have recently been developed to assess the species-richness of highly diverse, neglected invertebrate taxa. These methods, however, need to be tested under a variety of conditions, including the use of different markers and parameters. Here, we explored the species diversity of a species-rich group of braconid parasitoid wasps, the Neotropical genus Notiospathius, including 233 specimens from 10 different countries. We examined sequences of two mitochondrial (mt) (COI, cyt b) and one nuclear (wg) gene fragments. We analysed them separately as well as concatenating the mt data with the general mixed Yule-coalescent (GMYC) model for species delineation using different tree-building methods and parameters for reconstructing ultrametric trees. We evaluated the performance of GMYC analyses by comparing their species delineations with our morphospecies identifications. Reconstructing ultrametric trees with a relaxed lognormal clock rate using the program BEAST gave the most congruent results with morphology for the two mt markers. A tree obtained with wg using the programs MrBayes+Pathd8 had the fewest cases of incongruence with morphology, though the performance of this nuclear marker was considerably lower than that of COI and cyt b. Species delimitation using the coalescent prior to obtain ultrametric trees was morphologically more congruent with COI, whereas the Yule prior was more congruent with cyt b. The analyses concatenating the mt datasets failed to recover some species supported both by morphology and the separate analyses of the mt markers. The highest morphological congruence was obtained with the GMYC analysis on an ultrametric tree reconstructed with cyt b using the relaxed lognormal clock rate and the Yule prior, thus supporting the importance of using alternative markers when the information of the barcoding locus (COI) is not concordant with morphological evidence. Seventy-one species were delimited based on the congruence found among COI, cyt b and morphology. Both mt markers also revealed the existence of seven potential cryptic species. This high species richness from a scattered geographical sampling indicates that there is a remarkable number of Notiospathius species that remains undiscovered.