The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state

Trends in the incidence of pulmonary nodules in chest computed tomography:10-year results from two Dutch hospitals

Objective: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. Methods: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. Results: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. Conclusion: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. Clinical relevance statement: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. Key Points: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.</p

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain. It was collected from four different hospitals and was divided into a training (179 patients) and validation (39 patients) set. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. We set up a continuous segmentation challenge to allow for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation, and improve the diagnostic value of lumbar spine MRI

Glenohumeral joint injection: a comparative study of ultrasound and fluoroscopically guided techniques before MR arthrography

To assess the variability in accuracy of contrast media introduction, leakage, required time and patient discomfort in four different centres, each using a different image-guided glenohumeral injection technique. Each centre included 25 consecutive patients. The ultrasound-guided anterior (USa) and posterior approach (USp), fluoroscopic-guided anterior (FLa) and posterior (FLp) approach were used. Number of injection attempts, effect of contrast leakage on diagnostic quality, and total room, radiologist and procedure times were measured. Pain was documented with a visual analogue scale (VAS) pain score. Access to the joint was achieved in all patients. A successful first attempt significantly occurred more often with US (94%) than with fluoroscopic guidance (72%). Leakage of contrast medium did not cause interpretative difficulties. With US guidance mean room, procedure and radiologist times were significantly shorter (p < 0.001). The USa approach was rated with the lowest pre- and post-injection VAS scores. The four image-guided injection techniques are successful in injection of contrast material into the glenohumeral joint. US-guided injections and especially the anterior approach are significantly less time consuming, more successful on the first attempt, cause less patient discomfort and obviate the need for radiation and iodine contrast

Reduction of contrast medium volume in abdominal aorta CTA: Multiphasic injection technique versus a test bolus volume

Objective:\ud The purpose of this study is to reduce the administered contrast medium volume in abdominal CTA by using a test bolus injection, with the preservation of adequate quantitative and qualitative vessel enhancement.\ud \ud Study design:\ud For this technical efficacy study 30 patients, who were referred for a CTA examination of the abdominal aorta, were included. Randomly 15 patients were assigned to undergo a multiphasic injection protocol and received 89 mL of contrast medium (Optiray 350) (protocol I). Fifteen patients were assigned to the test bolus injection protocol (protocol II), which implies injection of a 10 mL test bolus of Optiray 350 prior to performing CTA with a 40 mL of contrast medium. Quantitative assessment of vascular enhancement was performed by measuring the amount of Hounsfield Units in the aorta at 30 positions from the celiac trunk to the iliac arteries in both groups. Qualitative assessment was performed by three radiologists who scored the images at a 5-point scale.\ud \ud Results:\ud Quantitative assessment showed that there was no significant difference in vascular enhance- ment for patients between the two protocols, with mean attenuation values of 280.9±50.84HU and 258.60 ± 39.28 HU, respectively. The image quality of protocol I was rated 4.31 (range: 3.67/5.00) and of protocol II 4.11 (range: 2.67/5.00). These differences were not statistically significant.\ud \ud Conclusion:\ud This study showed that by using a test bolus injection and the administration of 50 mL of con- trast medium overall, CTA of the abdominal aorta can reliably be performed, with regard to quantitative and qualitative adequate vessel enhancement

Reduced contrast medium in abdominal aorta CTA using a multiphasic injection technique

Purpose: The purpose of this study was to determine if with a multiphasic injection technique the admin- istered amount of contrast medium for abdominal computerized tomographic angiography (CTA) can be decreased, whilst improving CT image quality. Materials and methods: In 30 patients a multiphasic injection method was compared to the standard uniphasic contrast medium injection protocol. Fifteen patients underwent abdominal CTA with a standard uniphasic injection protocol (protocol I) receiving 100 mL of a non-ionic radiopaque contrast agent (Iover- sol). The second group of 15 patients underwent CTA with a multiphasic injection protocol (protocol II) receiving a total of 89 mL Ioversol. Vascular contrast enhancement and difference in enhancement uni- formity were assessed quantitatively and image quality was assessed by three independent radiologists. Results: Quantitative assessment of the vascular contrast enhancement showed that there was no signif- icant difference in enhancement uniformity for patients between the protocols. The image quality was rated as being good to excellent in 81.8% and 88.0% of the scans, for protocol I and protocol II, respectively. However these differences were not statistically significant.\ud Conclusion: By using a multiphasic injection technique with CTA of the abdominal aorta a reduction of 11 percent of contrast medium can be realized. Enhancement patterns are quantitatively as well as qualitatively comparable to the standard contrast medium injection protocol

Initial spreading of a mega feeder nourishment : Observations of the Sand Engine pilot project

Sand nourishments are a widely applied technique to increase beach width for recreation or coastal safety. As the size of these nourishments increases, new questions arise on the adaptation of the coastal system after such large unnatural shapes have been implemented. This paper presents the initial morphological evolution after implementation of a mega-nourishment project at the Dutch coast intended to feed the surrounding beaches. In total 21.5 million m3 dredged material was used for two shoreface nourishments and a large sandy peninsula. The Sand Engine peninsula, a highly concentrated nourishment of 17 million m3 of sand in the shape of a sandy hook and protruding 1 km from shore, was measured intensively on a monthly scale in the first 18 months after completion. We examine the rapid bathymetric evolution with concurrent offshore wave forcing to investigate the feeding behaviour of the nourishment to the adjacent coast. Our observations show a large shoreline retreat of O (150 m) along the outer perimeter of the peninsula, with locally up to 300 m retreat. The majority (72%) of the volumetric losses in sediment on the peninsula (1.8 million m3) were compensated by accretion on adjacent coastal sections and dunes, confirming the feeding property of the mega nourishment. Further analyses show that the morphological changes were most pronounced in the first 6 months while the planform curvature reduced and the surf zone slope flattened to pre-nourishment values. In the following 12 months the changes were more moderate. Overall, the feeding property was strongly correlated to incident wave forcing, such that months with high incoming waves resulted in more alongshore spreading. Months with small wave heights resulted in minimal change in sediment distribution alongshore and mostly cross-shore movement of sediment

Injection of the subacromial-subdeltoid bursa: blind or ultrasound-guided?

Contains fulltext : 53255.pdf (publisher's version ) (Open Access)BACKGROUND: Blind injection of the subacromial-sub-deltoid bursa (SSB) for diagnostic purposes (Neer test) or therapeutic purposes (corticosteroid therapy) is frequently used. Poor response to previous blind injection or side effects may be due to a misplaced injection. It is assumed that ultrasound (US)-guided injections are more accurate than blind injections. In a randomized study, we compared the accuracy of blind injection to that of US-guided injection into the SSB. PATIENTS AND METHODS: 20 consecutive patients with impingement syndrome of the shoulder were randomized for blind or US-guided injection in the SSB. Injection was performed either by an experienced orthopedic surgeon or by an experienced musculoskeletal radiologist. A mixture of 1 m'L methylprednisolone acetate, 4 mL prilocaine hydrochloride and 0.02 mL (0.01 mmol) Gadolinium DTPA was injected. Immediately after injection, a 3D-gradient T1-weighted magnetic resonance scan of the shoulder was performed. The location of the injected fluid was independently assessed by 2 radiologists who were blinded as to the injection technique used. RESULTS: The accuracy of blind and US-guided injection was the same. The fluid was injected into the bursa in all cases. INTERPRETATION: Blind injection into the SSB is as reliable as US-guided injection and could therefore be used in daily routine. US-guided injections may offer a useful alternative in difficult cases, such as with changed anatomy postoperatively or when there is no effective clinical outcome

Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist

Objectives: To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs. Methods: Four datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen’s kappa coefficient (κ), fracture localization precision, and reading time. Results: The algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists’ mean], p ≥.05). AI assistance improved five out of ten pairs of inter-observer Cohen’s κ agreements (p <.05) and reduced reading time in four radiologists (p <.001), but did not improve other metrics in the majority of radiologists (p ≥.05). Conclusions: The AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time. Key Points: • An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists

Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist

Objectives: To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs. Methods: Four datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen’s kappa coefficient (κ), fracture localization precision, and reading time. Results: The algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists’ mean], p ≥.05). AI assistance improved five out of ten pairs of inter-observer Cohen’s κ agreements (p <.05) and reduced reading time in four radiologists (p <.001), but did not improve other metrics in the majority of radiologists (p ≥.05). Conclusions: The AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time. Key Points: • An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists