Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images.

Objective: To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA). Methods: One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA. Data was divided into a training, validation, and test set. The AI model was trained on the training data to perform bone, synovium, and osteophyte identification on the images. Based on the manually performed image segmentation, an AI was trained to classify the severity of osteophytes according to EOGS from 0 to 3. Percent Exact Agreement (PEA) and Percent Close Agreement (PCA) were assessed on individual joints and overall. PCA allows a difference of one EOGS grade between doctor assessment and AI. Results: A total of 4615 ultrasound images were used for AI development and testing. The developed AI model scored on the test set for the MCP joints a PEA of 76% and PCA of 97%; for PIP, a PEA of 70% and PCA of 97%; for DIP, a PEA of 59% and PCA of 94%, and CMC a PEA of 50% and PCA of 82%. Combining all joints, we found a PEA between AI and doctor assessments of 68% and a PCA of 95%. Conclusion: The developed AI model can perform joint ultrasound image segmentation and severity scoring of osteophytes, according to the EOGS. As proof of concept, this first version of the AI model is successful, as the agreement performance is slightly higher than previously found agreements between experts when assessing osteophytes on hand OA ultrasound images. The segmentation of the image makes the AI explainable to the doctor, who can immediately see why the AI applies a given score. Future validation in hand OA cohorts is necessary though.

A novel ultrasound software system for lumbar level identification in obstetric patients.

PURPOSE: Using machine learning, we developed a proprietary ultrasound software called the Spine Level Identification (SLIDE) system, which automatically identifies lumbar landmarks in real time as the operator slides the transducer over the lumber spine. Here, we assessed the agreement between SLIDE and manual palpation and traditional lumbar ultrasound (LUS) for determining the primary target L3-4 interspace. METHODS: Upon institutional ethics approval and informed consent, 76 healthy term parturients scheduled for elective Caesarean delivery were recruited. The L3-4 interspace was identified by manual palpation and then by the SLIDE method. The reference standard was located using traditional LUS by an experienced operator. The primary outcome was the L3-4 interspace identification agreement of manual palpation and SLIDE with the reference standard, as percentage agreement and Gwet's agreement coefficient (AC1). RESULTS: The raw agreement was 70% with Gwet's agreement coefficient (AC1) = 0.59 (95% confidence interval [CI], 0.41 to 0.77) for manual palpation and 84% with Gwet's AC1 = 0.82 (95% CI, 0.70 to 0.93) for SLIDE. When the levels differ from the reference, the manual palpation method identified L2-3 more often than L4-5 while the SLIDE method identified equally above or below L3-4. The SLIDE system had greater agreement than palpation in locating L3-4 and all other lumber interspaces after controlling for body mass index (adjusted odds ratio, 2.99; 95% CI, 1.21 to 8.7; P = 0.02). CONCLUSION: The SLIDE system had higher agreement with traditional ultrasound than manual palpation did in identifying L3-4 and all other lumber interspaces after adjusting for BMI in healthy term obstetric patients. Future studies should examine factors that affect agreement and ways to improve SLIDE for clinical integration. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT02982317); registered 5 December 2016.

RéSUMé: OBJECTIF: À l'aide de l'apprentissage automatique, nous avons développé un logiciel d'échographie propriétaire appelé SLIDE (pour Spine Level Identification, c.-à-d. système d'identification du niveau vertébral), qui identifie automatiquement les points de repère lombaires en temps réel lorsque l'opérateur fait passer le transducteur sur la colonne lombaire. Ici, nous avons évalué l'agrément entre le SLIDE et la palpation manuelle et l'échographie lombaire traditionnelle pour déterminer l'espace intervertébral cible principal L3­L4. MéTHODE: Après avoir obtenu l'approbation du comité d'éthique de l'établissement et le consentement éclairé, 76 parturientes en bonne santé et à terme devant bénéficier d'un accouchement par césarienne programmée ont été recrutées. L'espace intervertébral L3­L4 a été identifié par palpation manuelle puis avec le logiciel SLIDE. L'étalon de référence a été localisé à l'aide d'une échographie lombaire traditionnelle par un opérateur expérimenté. Le critère d'évaluation principal était l'agrément entre l'identification de l'espace intervertébral L3­L4 par palpation manuelle et par logiciel SLIDE avec l'étalon de référence, en pourcentage d'agrément et coefficient d'agrément de Gwet (CA1). RéSULTATS: L'agrément brut était de 70 % avec le coefficient d'agrément de Gwet (CA1) = 0,59 (intervalle de confiance [IC] à 95 %, 0,41 à 0,77) pour la palpation manuelle et de 84 % avec le CA1 de Gwet = 0,82 (IC 95 %, 0,70 à 0,93) pour le logiciel SLIDE. Lorsque les niveaux lombaires différaient de la référence, la méthode de palpation manuelle a identifié L2­L3 plus souvent que L4­L5, tandis que la méthode SLIDE a identifié les vertèbres supérieures ou inférieures à L3­L4 de manière égale. Le système SLIDE a affiché un agrément plus important que la palpation pour localiser L3­L4 et tous les autres espaces intervertébraux lombaires après ajustement pour tenir compte de l'indice de masse corporelle (rapport de cotes ajusté, 2,99; IC 95 %, 1,21 à 8,7; P = 0,02). CONCLUSION: Le système SLIDE avait affiché un agrément plus élevé avec l'échographie traditionnelle que la palpation manuelle pour identifier le niveau L3­L4 et tous les autres espaces intervertébraux lombaires après ajustement pour tenir compte de l'IMC chez les patientes obstétricales à terme en bonne santé. Une étude future devrait examiner les facteurs qui affectent l'agrément et les moyens d'améliorer le logiciel SLIDE pour une intégration clinique. ENREGISTREMENT DE L'éTUDE: www.clinicaltrials.gov (NCT02982317); enregistrée le 5 décembre 2016.

Ultrasound joint examination by an automated system versus by a rheumatologist: from a patient perspective.

BACKGROUND: The Arthritis Ultrasound Robot (ARTHUR) is an automated system for ultrasound scanning of the joints of both hands and wrists, with subsequent disease activity scoring using artificial intelligence. The objective was to describe the patient's perspective of being examined by ARTHUR, compared to an ultrasound examination by a rheumatologist. Further, to register any safety issues with the use of ARTHUR. METHODS: Twenty-five patients with rheumatoid arthritis (RA) had both hands and wrists examined by ultrasound, first by a rheumatologist and subsequently by ARTHUR. Patient-reported outcomes (PROs) were obtained after the examination by the rheumatologist and by ARTHUR. PROs regarding pain, discomfort and overall experience were collected, including willingness to be examined again by ARTHUR as part of future clinical follow-up. All ARTHUR examinations were observed for safety issues. RESULTS: There was no difference in pain or discomfort between the examination by a rheumatologist and by ARTHUR (p = 0.29 and p = 0.20, respectively). The overall experience of ARTHUR was described as very good or good by 92% (n = 23), with no difference compared to the examination by the rheumatologist (p = 0.50). All (n = 25) patients were willing to be examined by ARTHUR again, and 92% (n = 23) would accept ARTHUR as a regular part of their RA clinical follow up. No safety issues were registered. CONCLUSIONS: Joint ultrasound examination by ARTHUR was safe and well-received, with no difference in PRO components compared to ultrasound examination by a rheumatologist. Fully automated systems for RA disease activity assessment could be important in future strategies for managing RA patients. TRIAL REGISTRATION: The study was evaluated by the regional ethics committee (ID: S-20200145), which ruled it was not a clinical trial necessary for their approval. It was a quality assessment project, as there was no intervention to the patient. The study was hereafter submitted and registered to Odense University Hospital, Region of Southern Denmark as a quality assessment project and approved (ID: 20/55294).

Ultrasound joint examination by an automated system versus by a rheumatologist: from a patient perspective

Abstract Background: The Arthritis Ultrasound Robot (ARTHUR) is an automated system for ultrasound scanning of the joints of both hands and wrists, with subsequent disease activity scoring using artificial intelligence. The objective was to describe the patient's perspective of being examined by ARTHUR, compared to an ultrasound examination by a rheumatologist. Further, to register any safety issues with the use of ARTHUR. Methods: Twenty-five patients with rheumatoid arthritis (RA) had both hands and wrists examined by ultrasound, first by a rheumatologist and subsequently by ARTHUR. Patient-reported outcomes (PROs) were obtained after the examination by the rheumatologist and by ARTHUR. PROs regarding pain, discomfort and overall experience were collected, including willingness to be examined again by ARTHUR as part of future clinical follow-up. All ARTHUR examinations were observed for safety issues. Results: There was no difference in pain or discomfort between the examination by a rheumatologist and by ARTHUR ( p =0.29 and p =0.20, respectively). The overall experience of ARTHUR was described as very good or good by 92% (n =23), with no difference compared to the examination by the rheumatologist ( p =0.50). All (n =25) patients were willing to be examined by ARTHUR again, and 92% (n =23) would accept ARTHUR as a regular part of their RA clinical follow up. No safety issues were registered. Conclusion: Joint ultrasound examination by ARTHUR was safe and well-received, with no difference in PRO components compared to ultrasound examination by a rheumatologist. Fully automated systems for RA disease activity assessment could be important in future strategies for managing RA patients. Trial registration: The study was evaluated by the regional ethics committee (ID: S-20200145), which ruled it was not a clinical trial necessary for their approval. It was a quality assessment project, as there was no intervention to the patient. The study was hereafter submitted and registered to Odense University Hospital, Region of Southern Denmark as a quality assessment project and approved (ID: 20/55294).

Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry.

OBJECTIVES: To evaluate the accuracy of an automated three-dimensional (3D) ultrasound technique for fetal intracranial measurements compared with manual acquisition. METHODS: This was a prospective observational study of patients presenting for routine anatomical survey between 18 + 0 and 22 + 6 weeks' gestation. After providing informed consent, each patient underwent two consecutive ultrasound examinations of the fetal head, one by a sonographer and one by a physician. Each operator obtained manual measurements of the biparietal diameter (BPD), head circumference (HC), transcerebellar diameter (TCD), cisterna magna (CM) and posterior horn of the lateral ventricle (Vp), followed by automated measurements of these structures using an artificial intelligence-based tool, SonoCNS® Fetal Brain. Both operators repeated the automated approach until all five measurements were obtained in a single sweep, up to a maximum of three attempts. The accuracy of automated measurements was compared with that of manual measurements using intraclass correlation coefficients (ICC) by operator type, accounting for patient and ultrasound characteristics. RESULTS: One hundred and forty-three women were enrolled in the study. Median body mass index was 24.0 kg/m2 (interquartile range (IQR), 22.5-26.8 kg/m2 ) and median subcutaneous thickness was 1.6 cm (IQR, 1.3-2.0 cm). Fifteen (10%) patients had at least one prior Cesarean delivery, 17 (12%) had other abdominal surgery and 78 (55%) had an anterior placenta. Successful acquisition of the automated measurements was achieved on the first, second and third attempts in 70%, 22% and 3% of patients, respectively, by sonographers and in 76%, 16% and 3% of cases, respectively, by physicians. The automated algorithm was not able to identify and measure all five structures correctly in six (4%) and seven (5%) patients scanned by the sonographers and physicians, respectively. The ICCs reflected good reliability (0.80-0.88) of the automated compared with the manual approach for BPD and HC and poor to moderate reliability (0.23-0.50) for TCD, CM and Vp. Fetal lie, head position, placental location, maternal subcutaneous thickness and prior Cesarean section were not associated with the success or accuracy of the automated technique. CONCLUSIONS: Automated 3D ultrasound imaging of the fetal head using SonoCNS reliably identified and measured BPD and HC but was less consistent in accurately identifying and measuring TCD, CM and Vp. While these results are encouraging, further optimization of the automated technology is necessary prior to incorporation of the technique into routine sonographic protocols. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Comparison of automated versus hand-held breast US in supplemental screening in asymptomatic women with dense breasts: is there a difference regarding woman preference, lesion detection and lesion characterization?

PURPOSE: To compare automated breast volumetric scanning (ABVS) with hand-held bilateral whole breast ultrasound (HHUS) prospectively in regards to patient workflow, woman preference, efficacy in lesion detection, and characterization. MATERIALS AND METHODS: Supplemental screening was performed with both ABVS and HHUS to 345 women with dense breasts and negative mammograms. Acquisition and evaluation times were recorded. Lesions were classified according to BIRADS US criteria and compared one to one. Women were recalled for a secondary HHUS examination if ABVS showed any additional lesions. Findings were compared based on biopsy results and/or 36-48 months of follow-up. RESULTS: Findings could be compared for 340 women. There were two carcinomas which were detected by both methods, with no interval cancers in the follow-up period. Recall rate was 46/340 (13.05%) for ABVS and 4/340 (1.18%) for HHUS. ABVS recalls decreased with experience. HHUS had more true negative (BIRADS 1-2) results, while ABVS had more false positive ones (p < 0.001). Positive predictive value was 4.17% for ABVS and 50% for HHUS. ABVS overdiagnosed shadowings (p < 0.01), distortions (p < 0.034), and irregular nodules (p < 0.001) in comparison to HHUS. At ABVS, 10.6% of women experienced severe pain. 59.7% stated that they would choose HHUS if they had the chance. CONCLUSION: ABVS is as good as HHUS in lesion detection. However, the recall rate is higher and positive predictive value is lower with ABVS, which could result in more follow-ups, and more anxiety for the women. More than 50% women stated they would prefer HHUS if they were given the chance.

Recent Development in Three and Four Dimension Fetal Echocardiography.

Since its introduction >15 years ago, the use of spatial and temporal image correlation (STIC) technology has contributed substantially to fetal echocardiography. Moreover, significant advances have occurred in 3- and 4-dimensional (3D/4D) echocardiography over the past several years including the matrix probe along with advances in gray scale and color Doppler post processing, resulting in improved display of ultrasound images. In this article, we provide examples to show these recent developments including the use of color Doppler with STIC in the glass-body mode and the matrix probe thus enabling the demonstration of cardiac anomalies of the 4-chamber-view and great arteries. The use of the matrix probe allows the examination of cardiac structures in 2 orthogonal planes simultaneously, which can help in display of anatomy side by side (Biplane mode). In addition, the rapid image reconstruction of the matrix probe allows for the display of live 4D and the rapid acquisition of a STIC volume. The display of multiplanar images of the heart in 3D/4D has also been used to automate the display of ultrasound images, resulting in standardization of the image display and thus minimizing the operation dependency of the ultrasound examination. Future addition of image recognition software can also provide assistance in image review.

Semi-automated ultrasound guidance applied to nasogastrojejunal tube replacement for enteral nutrition in critically ill adults.

BACKGROUND AND OBJECTIVE: At present, the enteral nutrition approaches via nose and duodenum (or nose and jejunum) are the preferred method of nutritional support in the medical engineering field, given the superiority of in line with physiological processes and no serious complication. In this study, the authors adopted saline as the acoustic window, and gave enteral nutrition support to critically ill patients, via the nasogastrojejunal approach guided by semi-automated ultrasound. These above patients benefited a lot from this kind of nutrition support treatment, and we aimed to report the detailed information. METHODS: Critically ill patients (n = 41) who had been treated with hospitalized intestine nutrition were identified. The Apogee 1200 ultrasonic diagnostic apparatus, and nasogastrojejunal tubes were adopted to carry out intestine nutrition treatment guided by semi-automated ultrasound. In order to confirm the specific positions of cardia, gastric body, antrum of stomach, and pylorus, the semi-automated ultrasound was utilized to probe the stomach cavity. And then, the ultrasonic probe was placed in the cardia location, and the nasogastrojejunal tube was slowly inserted through the metal thread. After operation, the nursing service satisfaction of patients and mean operation time were calculated, respectively. RESULTS: All the patients were treated with enteral nutrition via nasogastrojejunal tube, and the whole procedure was under the guidance of semi-automated ultrasonography. The end of the feeding tube is attached to the surface of the stomach with a greater curvature, which can be bent on account of a no gastric peristalsis squeeze function, and thereby were prevented from entering into the antrum and pylorus locations. After this procedure, the mental thread was taken out, and the tube was pushed forward by a "drift" approach in order to allow it to enter into the intestine. The total nursing service satisfaction of patients was 90.24%, and the total incidence of adverse reactions was 17.07%. CONCLUSIONS: In summary, the application of saline can be taken as sound window, and the metal wire as the tracking target, the bedside nasogastrojejunal tube guided by semi-automated ultrasound is an effective feeding tube placement method, with relatively good clinical application value in medical engineering.

Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review.

Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.

Training and standards for performance, interpretation, and structured reporting for supplemental breast cancer screening.

OBJECTIVE. To compensate for the reduction of mammography's sensitivity in women with dense breasts, supplemental screening can increase the cancer detection rate. The modalities suggested are MRI, which is the most sensitive and is indicated for women with the highest risk of breast cancer, and ultrasound, which is suggested for dense-breasted average-risk women. CONCLUSION. For decades, ultrasound has been a focused examination. Extending a handheld ultrasound examination to depict the entire breast requires formal didactic training and hands-on scanning to learn suitable, efficient methods. Automated options also require intensive training in performance and interpretation.

Acoustic performance of mesh compression paddles for a multimodality breast imaging system.

A system incorporating automated 3-D ultrasound and digital X-ray tomosynthesis is being developed for improved breast lesion detection and characterization. The goal of this work is to develop and test candidates for a dual-modality mesh compression paddle. A Computerized Imaging Reference Systems (Norfork, VA, USA) ultrasound phantom with tilted low-contrast cylindrical objects was used. Polyester mesh fabrics (1- and 2-mm spacing), a high-density polyethylene filament grid (Dyneema, DSM Dyneema, Stanley, NC, USA) and a solid polymethylpentene (TPX; Mitsui Plastics, Inc., White Plains, NY) paddle were compared with no overlying structures using a GE Logic 9 with M12L transducer. A viscous gel provided coupling. The phantom was scanned 10 times over 9 cm for each configuration. Image volumes were analyzed for signal strength, contrast and contrast-to-noise ratio. X-ray tests confirmed X-ray transparency for all materials. By all measures, both mesh fabrics outperformed TPX and Dyneema, and there were essentially no differences between 2-mm mesh and unobstructed configurations.

Diagnostic performance of automated breast ultrasound as a replacement for a hand-held second-look ultrasound for breast lesions detected initially on magnetic resonance imaging.

To evaluate the diagnostic performance of automated breast ultrasound (ABUS) after breast magnetic resonance imaging (MRI) as a replacement for hand-held second-look ultrasound (HH-SLUS), we evaluated 58 consecutive patients with breast cancer who had additional suspicious lesions on breast MRI. All patients underwent HH-SLUS and ABUS. Three breast radiologists evaluated the detectability, location, characteristics and conspicuity of lesions on ABUS. We also evaluated inter-observer variability and compared the results with HH-SLUS results. Eighty additional suspicious lesions were identified on breast MRI. Fifteen of the 80 lesions (19%) were not detected on HH-SLUS. Eight of the 15 lesions (53%) were detected on ABUS, whereas the remaining 7 were not detected on ABUS. Among the 65 lesions detected on HH-SLUS, only 3 lesions were not detected on ABUS. The intra-class correlation coefficients for lesion location and size all exceeded 0.70, indicating high reliability. Moderate to fair agreement was found for mass shape, orientation, margin and Breast Imaging Reporting and Data System (BI-RADS) final assessment. Therefore, ABUS can reliably detect additional suspicious lesions identified on breast MRI and may help in the decision on biopsy guidance method (US vs. MRI) as a replacement tool for HH-SLUS.