Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application.

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.

Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation.

While many deep-learning-based computer-aided detection systems (CAD) have been developed and commercialized for abnormality detection in chest radiographs (CXR), their ability to localize a target abnormality is rarely reported. Localization accuracy is important in terms of model interpretability, which is crucial in clinical settings. Moreover, diagnostic performances are likely to vary depending on thresholds which define an accurate localization. In a multi-center, stand-alone clinical trial using temporal and external validation datasets of 1,050 CXRs, we evaluated localization accuracy, localization-adjusted discrimination, and calibration of a commercially available deep-learning-based CAD for detecting consolidation and pneumothorax. The CAD achieved image-level AUROC (95% CI) of 0.960 (0.945, 0.975), sensitivity of 0.933 (0.899, 0.959), specificity of 0.948 (0.930, 0.963), dice of 0.691 (0.664, 0.718), moderate calibration for consolidation, and image-level AUROC of 0.978 (0.965, 0.991), sensitivity of 0.956 (0.923, 0.978), specificity of 0.996 (0.989, 0.999), dice of 0.798 (0.770, 0.826), moderate calibration for pneumothorax. Diagnostic performances varied substantially when localization accuracy was accounted for but remained high at the minimum threshold of clinical relevance. In a separate trial for diagnostic impact using 461 CXRs, the causal effect of the CAD assistance on clinicians' diagnostic performances was estimated. After adjusting for age, sex, dataset, and abnormality type, the CAD improved clinicians' diagnostic performances on average (OR [95% CI] = 1.73 [1.30, 2.32]; p < 0.001), although the effects varied substantially by clinical backgrounds. The CAD was found to have high stand-alone diagnostic performances and may beneficially impact clinicians' diagnostic performances when used in clinical settings.

A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

PURPOSE: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. MATERIALS AND METHODS: In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. RESULTS: The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. CONCLUSION: The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.

Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation.

For compression fracture detection and evaluation, an automatic X-ray image segmentation technique that combines deep-learning and level-set methods is proposed. Automatic segmentation is much more difficult for X-ray images than for CT or MRI images because they contain overlapping shadows of thoracoabdominal structures including lungs, bowel gases, and other bony structures such as ribs. Additional difficulties include unclear object boundaries, the complex shape of the vertebra, inter-patient variability, and variations in image contrast. Accordingly, a structured hierarchical segmentation method is presented that combines the advantages of two deep-learning methods. Pose-driven learning is used to selectively identify the five lumbar vertebrae in an accurate and robust manner. With knowledge of the vertebral positions, M-net is employed to segment the individual vertebra. Finally, fine-tuning segmentation is applied by combining the level-set method with the previously obtained segmentation results. The performance of the proposed method was validated by 160 lumbar X-ray images, resulting in a mean Dice similarity metric of 91.60±2.22%. The results show that the proposed method achieves accurate and robust identification of each lumbar vertebra and fine segmentation of individual vertebra.

Contribution of SLC22A12 on hypouricemia and its clinical significance for screening purposes.

Differentiating between inherited renal hypouricemia and transient hypouricemic status is challenging. Here, we aimed to describe the genetic background of hypouricemia patients using whole-exome sequencing (WES) and assess the feasibility for genetic diagnosis using two founder variants in primary screening. We selected all cases (N = 31) with extreme hypouricemia (<1.3 mg/dl) from a Korean urban cohort of 179,381 subjects without underlying conditions. WES and corresponding downstream analyses were performed for the discovery of rare causal variants for hypouricemia. Two known recessive variants within SLC22A12 (p.Trp258*, pArg90His) were identified in 24 out of 31 subjects (77.4%). In an independent cohort, we identified 50 individuals with hypouricemia and genotyped the p.Trp258* and p.Arg90His variants; 47 of the 50 (94%) hypouricemia cases were explained by only two mutations. Four novel coding variants in SLC22A12, p.Asn136Lys, p.Thr225Lys, p.Arg284Gln, and p.Glu429Lys, were additionally identified. In silico studies predict these as pathogenic variants. This is the first study to show the value of genetic diagnostic screening for hypouricemia in the clinical setting. Screening of just two ethnic-specific variants (p.Trp258* and p.Arg90His) identified 87.7% (71/81) of Korean patients with monogenic hypouricemia. Early genetic identification of constitutive hypouricemia may prevent acute kidney injury by avoidance of dehydration and excessive exercise.

Acute hepatic encephalopathy presenting as cortical laminar necrosis: case report.

We report on a 55-year-old man with alcoholic liver cirrhosis who presented with status epilepticus. Laboratory analysis showed markedly elevated blood ammonia. Brain magnetic resonance imaging (MRI) showed widespread cortical signal changes with restricted diffusion, involving both temporo-fronto-parietal cortex, while the perirolandic regions and occipital cortex were uniquely spared. A follow-up brain MRI demonstrated diffuse cortical atrophy with increased signals on T1-weighted images in both the basal ganglia and temporal lobe cortex, representing cortical laminar necrosis. We suggest that the brain lesions, in our case, represent a consequence of toxic effect of ammonia.

Cerebellar infarction originating from vertebral artery stenosis caused by a hypertrophied uncovertebral joint.

We report a case of cerebellar infarction originating from vertebral artery stenosis caused by a hypertrophied uncovertebral joint. A 38-year-old man presented with sudden onset of headache, dizziness, and dysarthria. The magnetic resonance imaging scan of the brain revealed acute infarction in the right cerebellar hemisphere in the territory of the posterior inferior cerebellar artery (PICA) and superior cerebellar artery (SCA). Magnetic resonance, 3-dimensional computed tomographic, and conventional angiography revealed severe right vertebral artery stenosis by extrinsic compression of the hypertrophied right C5-C6 uncovertebral joint. The diagnosis was acute cerebellar infarction, which was probably caused by embolism from the right vertebral artery stenosis that was caused by the hypertrophied C5-C6 uncovertebral joint. C5-C6 anterior discectomy and fusion were performed together with direct uncovertebral joint decompression. Postoperative 3-dimensional computed tomographic angiography revealed improvement in antegrade filling in the right vertebral artery. The imaging findings for this patient and the pathogenesis of cerebellar infarction for our patient are discussed.