Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.

To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole-slide images (WSI) like pathologists' "low-power view" information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue-level validation, MSP AI-G showed better accuracy (91.0%) than that of conventional patch-based AI-G (PB AI-G) (89.8%). Importantly, MSP AI-G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI-G (0.861 ± 0.078) in tissue-level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198-555 samples of 143-206 patients in each institute). MSP AI-G had high diagnostic accuracy and robustness in multi-institutions. When pathologists selectively review specimens in which pathologist's diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.

Classification of large-scale image database of various skin diseases using deep learning.

PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. METHODS: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. RESULTS: The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. CONCLUSION: This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.

Generation of realistic virtual nodules based on three-dimensional spatial resolution in lung computed tomography: A pilot phantom study.

PURPOSE: The aim of this feasibility study using phantoms was to propose a novel method for obtaining computer-generated realistic virtual nodules in lung computed tomography (CT). METHODS: In the proposed methodology, pulmonary nodule images obtained with a CT scanner are deconvolved with the point spread function (PSF) in the scan plane and slice sensitivity profile (SSP) measured for the scanner; the resultant images are referred to as nodule-like object functions. Next, by convolving the nodule-like object function with the PSF and SSP of another (target) scanner, the virtual nodule can be generated so that it has the characteristics of the spatial resolution of the target scanner. To validate the methodology, the authors applied physical nodules of 5-, 7- and 10-mm-diameter (uniform spheres) included in a commercial CT test phantom. The nodule-like object functions were calculated from the sphere images obtained with two scanners (Scanner A and Scanner B); these functions were referred to as nodule-like object functions A and B, respectively. From these, virtual nodules were generated based on the spatial resolution of another scanner (Scanner C). By investigating the agreement of the virtual nodules generated from the nodule-like object functions A and B, the equivalence of the nodule-like object functions obtained from different scanners could be assessed. In addition, these virtual nodules were compared with the real (true) sphere images obtained with Scanner C. As a practical validation, five types of laboratory-made physical nodules with various complicated shapes and heterogeneous densities, similar to real lesions, were used. The nodule-like object functions were calculated from the images of these laboratory-made nodules obtained with Scanner A. From them, virtual nodules were generated based on the spatial resolution of Scanner C and compared with the real images of laboratory-made nodules obtained with Scanner C. RESULTS: Good agreement of the virtual nodules generated from the nodule-like object functions A and B of the phantom spheres was found, suggesting the validity of the nodule-like object functions. The virtual nodules generated from the nodule-like object function A of the phantom spheres were similar to the real images obtained with Scanner C; the root mean square errors (RMSEs) between them were 10.8, 11.1, and 12.5 Hounsfield units (HU) for 5-, 7-, and 10-mm-diameter spheres, respectively. The equivalent results (RMSEs) using the nodule-like object function B were 15.9, 16.8, and 16.5 HU, respectively. These RMSEs were small considering the high contrast between the sphere density and background density (approximately 674 HU). The virtual nodules generated from the nodule-like object functions of the five laboratory-made nodules were similar to the real images obtained with Scanner C; the RMSEs between them ranged from 6.2 to 8.6 HU in five cases. CONCLUSIONS: The nodule-like object functions calculated from real nodule images would be effective to generate realistic virtual nodules. The proposed method would be feasible for generating virtual nodules that have the characteristics of the spatial resolution of the CT system used in each institution, allowing for site-specific nodule generation.

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density.

OBJECTIVE: We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT. METHODS: The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis. RESULTS: In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT > 220 HU. CONCLUSION: Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy.

Use of Sugammadex in a Patient with Myotonic Dystrophy Undergoing Laparoscopic Cholecystectomy.

A 37-year-old female patient with myotonic dystrophy was scheduled for laparoscopic cholecystectomy for gall stone under general anesthesia with continuous propofol infusion. Rocuronium was administered with careful monitoring using TOF- Watch®, measuring train-of-four count (Tc), TOF ratio (Tr), and posttetanic count The total amount of rocuronium was 70 mg ; 0.6 mg .kg⁻1 for anesthetic induction and 0.3 mg .kg⁻1 when Tc exceeded 1. When the operation was completed, Tc was 4, Tr was uncountable and she showed reaction to calling her name. Then sugammadex 2 mg .kg⁻1, rapidly antagonized the neuromuscular block, such that the Tr recovered to 100% but tidal volume was 250 ml in 3 minutes. Additional dorsage of sugammadex, 2 mg .kg⁻1, was required for tidal volume to recover to 530 ml. After 20 minutes of first administration of sugammadex, we extubated the tracheal tube without respiratory depression. To avoid respiratory depression, we did not use postoperative opioids. Intraoperative transversus abdominis plane block and postoperative thoracic epidural block with ropivacaine were successful for postoperative pain relief.

Technical Note: Image filtering to make computer-aided detection robust to image reconstruction kernel choice in lung cancer CT screening.

PURPOSE: In lung cancer computed tomography (CT) screening, the performance of a computer-aided detection (CAD) system depends on the selection of the image reconstruction kernel. To reduce this dependence on reconstruction kernels, the authors propose a novel application of an image filtering method previously proposed by their group. METHODS: The proposed filtering process uses the ratio of modulation transfer functions (MTFs) of two reconstruction kernels as a filtering function in the spatial-frequency domain. This method is referred to as MTFratio filtering. Test image data were obtained from CT screening scans of 67 subjects who each had one nodule. Images were reconstructed using two kernels: fSTD (for standard lung imaging) and fSHARP (for sharp edge-enhancement lung imaging). The MTFratio filtering was implemented using the MTFs measured for those kernels and was applied to the reconstructed fSHARP images to obtain images that were similar to the fSTD images. A mean filter and a median filter were applied (separately) for comparison. All reconstructed and filtered images were processed using their prototype CAD system. RESULTS: The MTFratio filtered images showed excellent agreement with the fSTD images. The standard deviation for the difference between these images was very small, ∼6.0 Hounsfield units (HU). However, the mean and median filtered images showed larger differences of ∼48.1 and ∼57.9 HU from the fSTD images, respectively. The free-response receiver operating characteristic (FROC) curve for the fSHARP images indicated poorer performance compared with the FROC curve for the fSTD images. The FROC curve for the MTFratio filtered images was equivalent to the curve for the fSTD images. However, this similarity was not achieved by using the mean filter or median filter. CONCLUSIONS: The accuracy of MTFratio image filtering was verified and the method was demonstrated to be effective for reducing the kernel dependence of CAD performance.

[Anesthetic Management for a Patient with Stiff-person Syndrome].

The stiff-person syndrome (SPS) is a rare autoimmune neurologic disorder that affects the gamma-aminobutyric acid (GABA) mediated inhibitory network in the central nervous system with anti-glutamic acid decarboxylase (GAD) antibodies. SPS is characterized by muscle rigidity and painful episodic spasms in axial and lower limb muscles. This case report describes successful peri-operative management of a 61-year-old female (height, 158 cm; weight, 60 kg, ASA-PS 2) with her right upper arm fracture who was scheduled for open reduction and internal fixation. This patient had bulbar paralysis, dysphagia and muscle rigidity associated with a high titer of anti-GAD auto antibodies (2,800 U x ml(-1)). She was diagnosed as SPS and has been treated with predonisolone (30 mg x day(-1)) and diazepam (20 mg x day(-1)) for 1 year. Predonisolone (15 mg) and diazepam (30 mg) was given orally before induction of general anesthesia with propofol, remifentanil and rocuronium bromide. Posture change from supine to beach-chair position led to sudden drop in blood pressure to 38/25 mmHg, which recovered promptly by injecting intravenous ephedrine hydrochloride (28 mg) and hydrocortisone (100 mg). Postanesthetic course was uneventful without postoperative neurologic abnormalities.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry.

With the wide dissemination of computed tomography (CT) screening for lung cancer, measuring the nodule volume accurately with computer-aided volumetry software is increasingly important. Many studies for determining the accuracy of volumetry software have been performed using a phantom with artificial nodules. These phantom studies are limited, however, in their ability to reproduce the nodules both accurately and in the variety of sizes and densities required. Therefore, we propose a new approach of using computer-simulated nodules based on the point spread function measured in a CT system. The validity of the proposed method was confirmed by the excellent agreement obtained between computer-simulated nodules and phantom nodules regarding the volume measurements. A practical clinical evaluation of the accuracy of volumetry software was achieved by adding simulated nodules onto clinical lung images, including noise and artifacts. The tested volumetry software was revealed to be accurate within an error of 20 % for nodules >5 mm and with the difference between nodule density and background (lung) (CT value) being 400-600 HU. Such a detailed analysis can provide clinically useful information on the use of volumetry software in CT screening for lung cancer. We concluded that the proposed method is effective for evaluating the performance of computer-aided volumetry software.

[Use of sugammadex in a patient with narrow angle glaucoma].

An 86-year-old woman was scheduled to receive fourth reconstructive surgery for femoral bone fracture under general anesthesia. She had been suspected with narrow angle glaucoma due to headache and bloodshot eyes during gastroscopy. During transfer to our hospital, she fell down and suffered from the right femoral neck fracture. The patient underwent femoral head replacement under spinal anesthesia. Later, she received surgeries twice uneventfully under spinal anesthesia; removal and re-implantation of the femoral bone head due to infection of the implanted head. Six months later, she fell down again and femoral bone was fractured during rehabilitation. Anesthesia was induced with propofol followed by rocuronium 0.9 mg x kg(-1) i.v. Anesthesia was maintained with propofol and remifentanil, and rocuronium was administered to maintain PTC of 10 or less. The surgery was completed in 150 minutes. At the end of surgery, a laryngeal mask was inserted and the tracheal tube was removed. TOF ratio recovered to 80% 8 minutes after sugammadex 2 mg kg(-1) i.v., and increased to 100% 3 minutes after additional 1 mg x kg(-1). Intraocular pressure stayed below 20 mmHg during the intervention. We could achieve full reversal of neuromuscular blockade and suppress increase in intraocular pressure with use of sugammadex.

Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening.

PURPOSE: While the acquisition of projection data in a computed tomography (CT) scanner is generally cqrried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening. METHODS: The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects. RESULTS: Filtered images for all combinations of three types of reconstruction kernels ("smooth," "standard," and "sharp" kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for the difference between filtered images and reconstructed images ranged from 1.9 to 23.5 Hounsfield units for all kernel combinations; these SDs were much smaller than the noise SDs in the reconstructed images. CONCLUSIONS: The proposed method has good performance and is clinically feasible in lung cancer screening. This method can be applied to images reconstructed on any scanner by measuring the PSFs in each system.

Volume-doubling time of pulmonary nodules with ground glass opacity at multidetector CT: Assessment with computer-aided three-dimensional volumetry.

RATIONALE AND OBJECTIVES: To investigate the volume-doubling time (VDT) of histologically proved pulmonary nodules showing ground glass opacity (GGO) at multidetector CT (MDCT) using computer-aided three-dimensional volumetry. MATERIALS AND METHODS: We retrospectively evaluated 47 GGO nodules (mixed n = 28, pure n = 19) that had been examined by thin-section helical CT more than once. They were histologically confirmed as atypical adenomatous hyperplasia (AAH, n = 13), bronchioloalveolar carcinoma (BAC, n = 22), and adenocarcinoma (AC, n = 12). Using computer-aided three-dimensional volumetry software, two radiologists independently performed volumetry of GGO nodules and calculated the VDT using data acquired from the initial and final CT study. We compared VDT among the three pathologies and also compared the VDT of mixed and pure GGO nodules. RESULTS: The mean VDT of all GGO nodules was 486.4 ± 368.6 days (range 89.0-1583.0 days). The mean VDT for AAH, BAC, and AC was 859.2 ± 428.9, 421.2 ± 228.4, and 202.1 ± 84.3 days, respectively; there were statistically significant differences for all comparative combinations of AAH, BAC, and AC (Steel-Dwass test, P < .01). The mean VDT for pure and mixed GGO nodules was 628.5 ± 404.2 and 276.9 ± 155.9 days, respectively; it was significantly shorter for mixed than pure GGO nodules (Mann-Whitney U-test, P < .01). CONCLUSION: The evaluation of VDT using computer-aided volumetry may be helpful in assessing the histological entities of GGO nodules.

[Spinal anesthesia in a patient with SMON disease].

We report a patient with subacute myelo-optico-neuropathy (SMON) in whom spinal anesthesia was employed to treat fracture of the femur neck. An 87-year-old woman was diagnosed as having SMON at the age of 45. The patient was admitted to our hospital with fracture of the femur neck. Aspiration pneumonia was also suspected with shadow in the right lung on the chest X-P The percutaneous oxygen saturation (Spo2) with room air was 77%. Spinal anesthesia with 5 mg of 0.5% hyperbaric bupivacaine and 20 mcg of fentanyl was performed at L3-4. The level of anesthesia was T4. During surgery, no severe pain in the lower limbs was observed. Three hours after the end of surgery, the level of anesthesia was T9. On the day after surgery, the extent of dysesthesia and reflex were similar to those before surgery. General anesthesia has been chosen in SMON patients, because there was a report of severe pain of the lower limbs after spinal anesthesia with dibucaine. In our patient, general anesthesia was considered inappropriate due to hypoxemia. We used a mixture of bupivacaine and fentanyl for spinal anesthesia, because the neurotoxicity of bupivacaine is weaker than that of dibucaine.

Computer-aided volumetry of pulmonary nodules exhibiting ground-glass opacity at MDCT.

OBJECTIVE: The purpose of this study was to investigate the accuracy and reproducibility of results acquired with computer-aided volumetry software during MDCT of pulmonary nodules exhibiting ground-glass opacity. MATERIALS AND METHODS: To evaluate the accuracy of computer-aided volumetry software, we performed thin-section helical CT of a chest phantom that included simulated 3-, 5-, 8-, 10-, and 12-mm-diameter ground-glass opacity nodules with attenuation of -800, -630, and -450 HU. Three radiologists measured the volume of the nodules and calculated the relative volume measurement error, which was defined as follows: (measured nodule volume minus assumed nodule volume / assumed nodule volume) x 100. Two radiologists performed two independent measurements of 59 nodules in humans. Intraobserver and interobserver agreement was evaluated with Bland-Altman methods. RESULTS: The relative volume measurement error for simulated ground-glass opacity nodules measuring 3 mm ranged from 51.1% to 85.2% and for nodules measuring 5 mm or more in diameter ranged from -4.1% to 7.1%. In the clinical study, for intraobserver agreement, the 95% limits of agreement were -14.9% and -13.7% and -16.6% to 15.7% for observers A and B. For interobserver agreement, these values were -16.3% to 23.7% for nodules 8 mm in diameter or larger. CONCLUSION: With computer-aided volumetry of ground-glass opacity nodules, the relative volume measurement error was small for nodules 5 mm in diameter or larger. Intraobserver and interobserver agreement was relatively high for nodules 8 mm in diameter or larger.

[Propofol triggers a marked body temperature increase in a patient with fulminant malignant hyperthermia (MH) without inducing other symptoms of MH].

A 53-year-old woman who had experienced symptoms of fulminant malignant hyperthermia (MH) by sevoflurane a week before and her MH muscle biopsy revealing positive later, underwent the right hemicolectomy under total intravenous anesthesia with propofol and fentanyl. The patient's body temperature increased at a rate of 0.6 degree C per 15 min from 37.5 to 39.4 degrees C, but other symptoms of MH, such as tachycardia, arrhythmia, acidemia, and hypoxemia, were obviously slight in comparison with those induced by sevoflurane. The body temperature decreased after discontinuation of propofol and administration of dantrorene injection. When the patient received continuous propofol infusion for the purpose of sedation in the intensive care unit again, the body temperature gradually increased to 40 degrees C. However, it decreased to 37.8 degrees C after discontinuation of propofol and dantrorene injection again. It is well recognized that propofol is not a MH trigger, but it shoud be noted that some MH patients could experience a hypermetabolic state, such as hyperthermia, even by propofol.