Prognosticating the outcome of intensive care in older patients-a narrative review.

Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment.

Fragment distance-guided dual-stream learning for automatic pelvic fracture segmentation.

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, we propose a novel dual-stream learning framework for the automatic segmentation and category labeling of pelvic fractures. Our method uniquely identifies pelvic fracture fragments in various quantities and locations using a dual-branch architecture that leverages distance learning from bone fragments. Moreover, we develop a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on three pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean Sensitivity of 0.935±0.068 and 0.929±0.058 in the dataset FracCLINIC, and 0.955±0.072 and 0.912±0.125 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures.

Three scans are better than two for follow-up: An automatic method for finding missed and misidentified lesions in cross-sectional follow-up of oncology patients.

PURPOSE: Missed and misidentified neoplastic lesions in longitudinal studies of oncology patients are pervasive and may affect the evaluation of the disease status. Two newly identified patterns of lesion changes, lone lesions and non-consecutive lesion changes, may help radiologists to detect these lesions. This study evaluated a new interpretation revision workflow of lesion annotations in three or more consecutive scans based on these suspicious patterns. METHODS: The interpretation revision workflow was evaluated on manual and computed lesion annotations in longitudinal oncology patient studies. For the manual revision, a senior radiologist and a senior neurosurgeon (the readers) manually annotated the lesions in each scan and later revised their annotations to identify missed and misidentified lesions with the workflow using the automatically detected patterns. For the computerized revision, lesion annotations were first computed with a previously trained nnU-Net and were then automatically revised with an AI-based method that automates the workflow readers' decisions. The evaluation included 67 patient studies with 2295 metastatic lesions in lung (19 patients, 83 CT scans, 1178 lesions), liver (18 patients, 77 CECT scans, 800 lesions) and brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions). RESULTS: Revision of the manual lesion annotations revealed 120 missed lesions and 20 misidentified lesions in 31 out of 67 (46%) studies. The automatic revision reduced the number of computed missed lesions by 55 and computed misidentified lesions by 164 in 51 out of 67 (76%) studies. CONCLUSION: Automatic analysis of three or more consecutive volumetric scans helps find missed and misidentified lesions and may improve the evaluation of temporal changes of oncological lesions.

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

PURPOSE: Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treatment response and the current disease status in the brain. This follow-up necessitates the comparison of target lesion sizes in pre- (prior) and post-SRS treatment (current) T1W-Gad MRI scans. Our aim was to evaluate SimU-Net, a novel deep-learning model for the detection and volumetric analysis of brain metastases and their temporal changes in paired prior and current scans. METHODS: SimU-Net is a simultaneous multi-channel 3D U-Net model trained on pairs of registered prior and current scans of a patient. We evaluated its performance on 271 pairs of T1W-Gad MRI scans from 226 patients who underwent SRS. An expert oncological neurosurgeon manually delineated 1,889 brain metastases in all the MRI scans (1,368 with diameters > 5 mm, 834 > 10 mm). The SimU-Net model was trained/validated on 205 pairs from 169 patients (1,360 metastases) and tested on 66 pairs from 57 patients (529 metastases). The results were then compared to the ground truth delineations. RESULTS: SimU-Net yielded a mean (std) detection precision and recall of 1.00±0.00 and 0.99±0.06 for metastases > 10 mm, 0.90±0.22 and 0.97±0.12 for metastases > 5 mm of, and 0.76±0.27 and 0.94±0.16 for metastases of all sizes. It improves lesion detection precision by 8% for all metastases sizes and by 12.5% for metastases < 10 mm with respect to standalone 3D U-Net. The segmentation Dice scores were 0.90±0.10, 0.89±0.10 and 0.89±0.10 for the above metastases sizes, all above the observer variability of 0.80±0.13. CONCLUSION: Automated detection and volumetric quantification of brain metastases following SRS have the potential to enhance the assessment of treatment response and alleviate the clinician workload.

The anatomical pattern of ductal arborization in parotid glands using cone-beam computerized sialography.

Cone-Beam Computed Tomography-Sialography (Sialo-CBCT) is used to demonstrate salivary ductal structure. This study aimed to conduct a volumetric analysis of the anatomical morphology of Normal-Appearing Glands (NAGs) in parotid sialo-CBCT. Our retrospective study included 14 parotid sialo-CBCT scans interpreted as NAGs in 11 patients with salivary gland impairment. The main duct length and width, as well as number and width of secondary and tertiary ducts were manually evaluated. We found that the main parotid duct showed an average width of 1.39 mm, 1.15 mm, and 0.98 mm, for the proximal, middle and distal thirds, respectively. The arborization pattern showed approximately 20% more tertiary (average number 11.1 ± 2.7) than secondary ducts (average number 9.0 ± 2.4) and approximately 8% narrower tertiary ducts (average width 0.65 ± 0.11 mm) compared to the secondary ducts (average width 0.77 ± 0.14 mm). Our anatomical analysis of NAGs in parotid sialo-CBCT demonstrated progressive narrowing of the main duct and increasing arborization and decreasing lumen size starting from the primary to the tertiary ducts. This is the most updated study regarding the anatomy of the parotid glands as demonstrated in sialo-CBCT. Our results may provide clinicians with the basic information for understanding aberration from normal morphology, as seen in salivary gland pathologies as well facilitate planning of treatment strategies, such as minimally invasive sialo-endoscopies, commonly practiced today.

Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up.

PURPOSE: Radiological follow-up of oncology patients requires the quantitative analysis of lesion changes in longitudinal imaging studies, which is time-consuming, requires expertise, and is subject to variability. This paper presents a comprehensive graph-based method for the automatic detection and classification of lesion changes in current and prior CT scans. METHODS: The inputs are the current and prior CT scans and their organ and lesion segmentations. Classification of lesion changes is formalized as bipartite graph matching where lesion pairings are computed by adaptive overlap-based lesion matching. Six types of lesion changes are computed by connected components analysis. The method was evaluated on 208 pairs of lung and liver CT scans from 57 patients with 4600 lesions, 1713 lesion matchings and 2887 lesion changes. Ground-truth lesion segmentations, lesion matchings and lesion changes were created by an expert radiologist. RESULTS: Our method yields a lesion matching rate accuracy of 99.7% (394/395) and 95.0% (1252/1318) for the lung and liver datasets. Precision and recall are > 0.99 and 0.94 and 0.95 (respectively) for the detection of lesion changes. The analysis of lesion changes helped the radiologist detect 48 missed lesions and 8 spurious lesions in the input ground-truth lesion datasets. CONCLUSION: The classification of lesion classification provides the clinician with a readily accessible and intuitive identification and classification of the lesion changes and their patterns in support of clinical decision making. Comprehensive automatic computer-aided lesion matching and analysis of lesion changes may improve quantitative follow-up and evaluation of disease status, assessment of treatment efficacy and response to therapy.

Automated quantification of glenoid bone loss in CT scans for shoulder dislocation surgery planning.

PURPOSE: Estimation of glenoid bone loss in CT scans following shoulder dislocation is required to determine the type of surgery needed to restore shoulder stability. This paper presents a novel automatic method for the computation of glenoid bone loss in CT scans. METHODS: The model-based method is a pipeline that consists of four steps: (1) computation of an oblique plane in the CT scan that best matches the glenoid face orientation; (2) selection of the glenoid oblique CT slice; (3) computation of the circle that best fits the posteroinferior glenoid contour; (4) quantification of the glenoid bone loss. The best-fit circle is computed with newly defined Glenoid Clock Circle Constraints. RESULTS: The pipeline and each of its steps were evaluated on 51 shoulder CT scans (44 patients). Ground truth oblique slice, best-fit circle, and glenoid bone loss measurements were obtained manually from three clinicians. The full pipeline yielded a mean absolute error (%) for the bone loss deficiency of 2.3 ± 2.9 mm (4.67 ± 3.32%). The mean oblique CT slice selection difference was 1.42 ± 1.32 slices, above the observer variability of 1.74 ± 1.82 slices. The glenoid bone loss deficiency measure (%) on the ground truth oblique glenoid CT slice has a mean average error of 0.54 ± 1.03 mm (4.76 ± 3.00%), close to the observer variability of 0.93 ± 1.40 mm (2.98 ± 4.97%). CONCLUSION: Our pipeline is the first fully automatic method for the quantitative analysis of glenoid bone loss in CT scans. The computed glenoid bone loss report may assist orthopedists in selecting and planning surgical shoulder dislocation procedures.

MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI.

PURPOSE: Radiological detection and follow-up of pancreatic cysts in multisequence MRI studies are required to assess the likelihood of their malignancy and to determine their treatment. The evaluation requires expertise and has not been automated. This paper presents MC3DU-Net, a novel multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI studies consisting of coronal MRCP and axial TSE MRI sequences. METHODS: MC3DU-Net leverages the information in both sequences by computing a pancreas Region of Interest (ROI) segmentation in the TSE MRI scan, transferring it to MRCP scan, and then detecting and segmenting the cysts in the ROI of the MRCP scan. Both the voxel-level ROI of the pancreas and the segmentation of the cysts are performed with 3D U-Nets trained with Hard Negative Patch Mining, a new technique for class imbalance correction and for the reduction in false positives. RESULTS: MC3DU-Net was evaluated on a dataset of 158 MRI patient studies with a training/validation/testing split of 118/17/23. Ground truth segmentations of a total of 840 cysts were manually obtained by expert clinicians. MC3DU-Net achieves a mean recall of 0.80 ± 0.19, a mean precision of 0.75 ± 0.26, a mean Dice score of 0.80 ± 0.19 and a mean ASSD of 0.60 ± 0.53 for pancreatic cysts of diameter > 5 mm, which is the clinically relevant endpoint. CONCLUSION: MC3DU-Net is the first fully automatic method for detection and segmentation of pancreatic cysts in MRI. Automatic detection and segmentation of pancreatic cysts in MRI can be performed accurately and reliably. It may provide a method for precise disease evaluation and may serve as a second expert reader.

Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR). METHODS: Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived. RESULTS: The method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10thpercentile and for 14 out of 17 FGR fetuses below the 3rd percentile. Six fetuses referred to MRI as AGA were found to be < 3rd percentile. CONCLUSIONS: The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. CLINICAL RELEVANCE STATEMENT: Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction. KEY POINTS: • An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.

Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI.

BACKGROUND AND PURPOSE: The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls. MATERIALS AND METHODS: We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria. RESULTS: In control fetuses, all parameters changed significantly with gestational age (P < .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (n = 33), with an area under the curve of 0.84 and a recall of 0.62. CONCLUSIONS: This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

Fetal MRI-Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification.

BACKGROUND: Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification. HYPOTHESIS: Smaller, leaner fetuses are malnourished and will experience unfavorable outcomes. STUDY TYPE: Prospective. POPULATION: 40 SGA fetuses, 26 (61.9%) females: 10/40 (25%) had obstetric interventions due to non-reassuring fetal status (NRFS), and 17/40 (42.5%) experienced adverse neonatal events (CANO). Participants underwent MRI between gestational ages 30 + 2 and 37 + 2. FIELD STRENGTH/SEQUENCE: 3-T, True Fast Imaging with Steady State Free Precession (TruFISP) and T1 -weighted two-point Dixon (T1 W Dixon) sequences. ASSESSMENT: Total body volume (TBV), fat signal fraction (FSF), and the fat-to-body volumes ratio (FBVR) were extracted from TruFISP and T1 W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded. STATISTICAL TESTS: Univariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. A P-value <0.05 was considered statistically significant. RESULTS: FBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2-0.76) and FSF (OR 0.95, CI 0.91-0.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56-0.86) and FSF (OR 0.96, CI 0.93-0.99) were linked to CANO. The FBVR sensitivity/specificity for obstetric interventions was 85.7%/87.5%, and the TBV sensitivity/specificity for CANO was 82.35%/86.4%. The sonographic criteria sensitivity/specificity for obstetric interventions was 100%/33.3% and insignificant for CANO (P = 0.145). DATA CONCLUSION: Reduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 5.

Real-time influence of intracellular acidification and Na+ /H+ exchanger inhibition on in-cell pyruvate metabolism in the perfused mouse heart: A 31 P-NMR and hyperpolarized 13 C-NMR study.

Disruption of acid-base balance is linked to various diseases and conditions. In the heart, intracellular acidification is associated with heart failure, maladaptive cardiac hypertrophy, and myocardial ischemia. Previously, we have reported that the ratio of the in-cell lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) activities is correlated with cardiac pH. To further characterize the basis for this correlation, these in-cell activities were investigated under induced intracellular acidification without and with Na+ /H+ exchanger (NHE1) inhibition by zoniporide. Male mouse hearts (n = 30) were isolated and perfused retrogradely. Intracellular acidification was performed in two ways: (1) with the NH4 Cl prepulse methodology; and (2) by combining the NH4 Cl prepulse with zoniporide. 31 P NMR spectroscopy was used to determine the intracellular cardiac pH and to quantify the adenosine triphosphate and phosphocreatine content. Hyperpolarized [1-13 C]pyruvate was obtained using dissolution dynamic nuclear polarization. 13 C NMR spectroscopy was used to monitor hyperpolarized [1-13 C]pyruvate metabolism and determine enzyme activities in real time at a temporal resolution of a few seconds using the product-selective saturating excitation approach. The intracellular acidification induced by the NH4 Cl prepulse led to reduced LDH and PDH activities (-16% and -39%, respectively). This finding is in line with previous evidence of reduced myocardial contraction and therefore reduced metabolic activity upon intracellular acidification. Concomitantly, the LDH/PDH activity ratio increased with the reduction in pH, as previously reported. Combining the NH4 Cl prepulse with zoniporide led to a greater reduction in LDH activity (-29%) and to increased PDH activity (+40%). These changes resulted in a surprising decrease in the LDH/PDH ratio, as opposed to previous predictions. Zoniporide alone (without intracellular acidification) did not change these enzyme activities. A possible explanation for the enzymatic changes observed during the combination of the NH4 Cl prepulse and NHE1 inhibition may be related to mitochondrial NHE1 inhibition, which likely negates the mitochondrial matrix acidification. This effect, combined with the increased acidity in the cytosol, would result in an enhanced H+ gradient across the mitochondrial membrane and a temporarily higher pyruvate transport into the mitochondria, thereby increasing the PDH activity at the expense of the cytosolic LDH activity. These findings demonstrate the complexity of in-cell cardiac metabolism and its dependence on intracellular acidification. This study demonstrates the capabilities and limitations of hyperpolarized [1-13 C]pyruvate in the characterization of intracellular acidification as regards cardiac pathologies.

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

OBJECTIVES: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis. METHODS: A total of 86 abdominal CT studies from 43 patients (prior and current scans) of abdominal CT scans of patients with 1041 liver metastases (mean = 12.1, std = 11.9, range 1-49) were analyzed. Two radiologists performed readings of all pairs; conventional with RECIST 1.1 and with computer-aided assessment. For computer-aided reading, we used a novel simultaneous multi-channel 3D R2U-Net classifier trained and validated on other scans. The reference was established by having an expert radiologist validate the computed lesion detection and segmentation. The results were then verified and modified as needed by another independent radiologist. The primary outcome measure was the disease status assessment with the conventional and the computer-aided readings with respect to the reference. RESULTS: For conventional and computer-aided reading, there was a difference in disease status classification in 40 out of 86 (46.51%) and 10 out of 86 (27.9%) CT studies with respect to the reference, respectively. Computer-aided reading improved conventional reading in 30 CT studies by 34.5% for two readers (23.2% and 46.51%) with respect to the reference standard. The main reason for the difference between the two readings was lesion volume differences (p = 0.01). CONCLUSIONS: AI-based computer-aided analysis of liver metastases may improve the accuracy of the evaluation of neoplastic liver disease status. CLINICAL RELEVANCE STATEMENT: AI may aid radiologists to improve the accuracy of evaluating changes over time in metastasis of the liver. KEY POINTS: • Classification of liver metastasis changes improved significantly in one-third of the cases with an automatically generated comprehensive lesion and lesion changes report. • Simultaneous deep learning changes detection and volumetric assessment may improve the evaluation of liver metastases temporal changes potentially improving disease management.

Reduced adipose tissue in growth-restricted fetuses using quantitative analysis of magnetic resonance images.

OBJECTIVES: Fat-water MRI can be used to quantify tissues' lipid content. We aimed to quantify fetal third trimester normal whole-body subcutaneous lipid deposition and explore differences between appropriate for gestational age (AGA), fetal growth restriction (FGR), and small for gestational age fetuses (SGAs). METHODS: We prospectively recruited women with FGR and SGA-complicated pregnancies and retrospectively recruited the AGA cohort (sonographic estimated fetal weight [EFW] ≥ 10th centile). FGR was defined using the accepted Delphi criteria, and fetuses with an EFW < 10th centile that did not meet the Delphi criteria were defined as SGA. Fat-water and anatomical images were acquired in 3 T MRI scanners. The entire fetal subcutaneous fat was semi-automatically segmented. Three adiposity parameters were calculated: fat signal fraction (FSF) and two novel parameters, i.e., fat-to-body volume ratio (FBVR) and estimated total lipid content (ETLC = FSF*FBVR). Normal lipid deposition with gestation and differences between groups were assessed. RESULTS: Thirty-seven AGA, 18 FGR, and 9 SGA pregnancies were included. All three adiposity parameters increased between 30 and 39 weeks (p < 0.001). All three adiposity parameters were significantly lower in FGR compared with AGA (p ≤ 0.001). Only ETLC and FSF were significantly lower in SGA compared with AGA using regression analysis (p = 0.018-0.036, respectively). Compared with SGA, FGR had a significantly lower FBVR (p = 0.011) with no significant differences in FSF and ETLC (p ≥ 0.053). CONCLUSIONS: Whole-body subcutaneous lipid accretion increased throughout the third trimester. Reduced lipid deposition is predominant in FGR and may be used to differentiate FGR from SGA, assess FGR severity, and study other malnourishment pathologies. CLINICAL RELEVANCE STATEMENT: Fetuses with growth restriction have reduced lipid deposition than appropriately developing fetuses measured using MRI. Reduced fat accretion is linked with worse outcomes and may be used for growth restriction risk stratification. KEY POINTS: • Fat-water MRI can be used to assess the fetal nutritional status quantitatively. • Lipid deposition increased throughout the third trimester in AGA fetuses. • FGR and SGA have reduced lipid deposition compared with AGA fetuses, more predominant in FGR.

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

The role of clinical phenotypes in decisions to limit life-sustaining treatment for very old patients in the ICU.

BACKGROUND: Limiting life-sustaining treatment (LST) in the intensive care unit (ICU) by withholding or withdrawing interventional therapies is considered appropriate if there is no expectation of beneficial outcome. Prognostication for very old patients is challenging due to the substantial biological and functional heterogeneity in that group. We have previously identified seven phenotypes in that cohort with distinct patterns of acute and geriatric characteristics. This study investigates the relationship between these phenotypes and decisions to limit LST in the ICU. METHODS: This study is a post hoc analysis of the prospective observational VIP2 study in patients aged 80 years or older admitted to ICUs in 22 countries. The VIP2 study documented demographic, acute and geriatric characteristics as well as organ support and decisions to limit LST in the ICU. Phenotypes were identified by clustering analysis of admission characteristics. Patients who were assigned to one of seven phenotypes (n = 1268) were analysed with regard to limitations of LST. RESULTS: The incidence of decisions to withhold or withdraw LST was 26.5% and 8.1%, respectively. The two phenotypes describing patients with prominent geriatric features and a phenotype representing the oldest old patients with low severity of the critical condition had the largest odds for withholding decisions. The discriminatory performance of logistic regression models in predicting limitations of LST after admission to the ICU was the best after combining phenotype, ventilatory support and country as independent variables. CONCLUSIONS: Clinical phenotypes on ICU admission predict limitations of LST in the context of cultural norms (country). These findings can guide further research into biases and preferences involved in the decision-making about LST. Trial registration Clinical Trials NCT03370692 registered on 12 December 2017.

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

A multi-view interactive virtual-physical registration method for mixed reality based surgical navigation in pelvic and acetabular fracture fixation.

PURPOSE: The treatment of pelvic and acetabular fractures remains technically demanding, and traditional surgical navigation systems suffer from the hand-eye mis-coordination. This paper describes a multi-view interactive virtual-physical registration method to enhance the surgeon's depth perception and a mixed reality (MR)-based surgical navigation system for pelvic and acetabular fracture fixation. METHODS: First, the pelvic structure is reconstructed by segmentation in a preoperative CT scan, and an insertion path for the percutaneous LC-II screw is computed. A custom hand-held registration cube is used for virtual-physical registration. Three strategies are proposed to improve the surgeon's depth perception: vertices alignment, tremble compensation and multi-view averaging. During navigation, distance and angular deviation visual cues are updated to help the surgeon with the guide wire insertion. The methods have been integrated into an MR module in a surgical navigation system. RESULTS: Phantom experiments were conducted. Ablation experimental results demonstrated the effectiveness of each strategy in the virtual-physical registration method. The proposed method achieved the best accuracy in comparison with related works. For percutaneous guide wire placement, our system achieved a mean bony entry point error of 2.76 ± 1.31 mm, a mean bony exit point error of 4.13 ± 1.74 mm, and a mean angular deviation of 3.04 ± 1.22°. CONCLUSIONS: The proposed method can improve the virtual-physical fusion accuracy. The developed MR-based surgical navigation system has clinical application potential. Cadaver and clinical experiments will be conducted in future.

Automated computation of radiographic parameters of distal radial metaphyseal fractures in forearm X-rays.

PURPOSE: Radiographic parameters (RPs) provide objective support for effective decision making in determining clinical treatment of distal radius fractures (DRFs). This paper presents a novel automatic RP computation pipeline for computing the six anatomical RPs associated with DRFs in anteroposterior (AP) and lateral (LAT) forearm radiographs. METHODS: The pipeline consists of: (1) segmentation of the distal radius and ulna bones with six 2D Dynamic U-Net deep learning models; (2) landmark points detection and distal radius axis computation from the segmentations with geometric methods; (3) RP computation and generation of a quantitative DRF report and composite AP and LAT radiograph images. This hybrid approach combines the advantages of deep learning and model-based methods. RESULTS: The pipeline was evaluated on 90 AP and 93 LAT radiographs for which ground truth distal radius and ulna segmentations and RP landmarks were manually obtained by expert clinicians. It achieves an accuracy of 94 and 86% on the AP and LAT RPs, within the observer variability, and an RP measurement difference of 1.4 ± 1.2° for the radial angle, 0.5 ± 0.6 mm for the radial length, 0.9 ± 0.7 mm for the radial shift, 0.7 ± 0.5 mm for the ulnar variance, 2.9 ± 3.3° for the palmar tilt and 1.2 ± 1.0 mm for the dorsal shift. CONCLUSION: Our pipeline is the first fully automatic method that accurately and robustly computes the RPs for a wide variety of clinical forearm radiographs from different sources, hand orientations, with and without cast. The computed accurate and reliable RF measurements may support fracture severity assessment and clinical management.

Verification, Evaluation, and Validation: Which, How & Why, in Medical Augmented Reality System Design.

This paper presents a discussion about the fundamental principles of Analysis of Augmented and Virtual Reality (AR/VR) Systems for Medical Imaging and Computer-Assisted Interventions. The three key concepts of Analysis (Verification, Evaluation, and Validation) are introduced, illustrated with examples of systems using AR/VR, and defined. The concepts of system specifications, measurement accuracy, uncertainty, and observer variability are defined and related to the analysis principles. The concepts are illustrated with examples of AR/VR working systems.