Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice

Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.

Clinical Application of Automatic Assessment of Scoliosis Cobb Angle Based on Deep Learning

INTRODUCTION

A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods.

METHODS

The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels.

RESULTS

The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification.

CONCLUSION

The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

Dual output feature fusion networks for femoral segmentation and quantitative analysis of the knee joint

BACKGROUND

Magnetic resonance imaging (MRI) is the preferred imaging modality for diagnosing knee disease. Segmentation of the knee MRI images is essential for subsequent quantification of clinical parameters and treatment planning for knee prosthesis replacement. However, the segmentation remains difficult due to individual differences in anatomy, the difficulty of obtaining accurate edges at lower resolutions, and the presence of speckle noise and artifacts in the images. In addition, radiologists must manually measure the knee's parameters which is a laborious and time-consuming process.

PURPOSE

Automatic quantification of femoral morphological parameters can be of fundamental help in the design of prosthetic implants for the repair of the knee and the femur. Knowledge of knee femoral parameters can provide a basis for femoral repair of the knee, the design of fixation materials for femoral prostheses, and the replacement of prostheses.

METHODS

This paper proposes a new deep network architecture to comprehensively address these challenges. A dual output model structure is proposed, with a high and low layer fusion extraction feature module designed to extract rich features through the cross-fusion mechanism. A multi-scale edge information extraction spatial feature module is also developed to address the boundary-blurring problem.

RESULTS

Based on the precise automated segmentation results, 10 key clinical parameters were automatically measured for a knee femoral prosthesis replacement program. The correlation coefficients of the quantitative results of these parameters compared to manual results all achieved at least 0.92. The proposed method was extensively evaluated with MRIs of 78 patients' knees, and it consistently outperformed other methods used for segmentation.

CONCLUSIONS

The automated quantization process produced comparable measurements to those manually obtained by radiologists. This paper demonstrates the viability of automatic knee MRI image segmentation and quantitative analysis with the proposed method. This provides data to support the accuracy of assessing the progression and biomechanical changes of osteoarthritis of the knee using an automated process, thus saving valuable time for the radiologists and surgeons.

Automatic Vertebral Rotation Angle Measurement of 3D Vertebrae Based on an Improved Transformer Network

The measurement of vertebral rotation angles serves as a crucial parameter in spinal assessments, particularly in understanding conditions such as idiopathic scoliosis. Historically, these angles were calculated from 2D CT images. However, such 2D techniques fail to comprehensively capture the intricate three-dimensional deformities inherent in spinal curvatures. To overcome the limitations of manual measurements and 2D imaging, we introduce an entirely automated approach for quantifying vertebral rotation angles using a three-dimensional vertebral model. Our method involves refining a point cloud segmentation network based on a transformer architecture. This enhanced network segments the three-dimensional vertebral point cloud, allowing for accurate measurement of vertebral rotation angles. In contrast to conventional network methodologies, our approach exhibits notable improvements in segmenting vertebral datasets. To validate our approach, we compare our automated measurements with angles derived from prevalent manual labeling techniques. The analysis, conducted through Bland-Altman plots and the corresponding intraclass correlation coefficient results, indicates significant agreement between our automated measurement method and manual measurements. The observed high intraclass correlation coefficients (ranging from 0.980 to 0.993) further underscore the reliability of our automated measurement process. Consequently, our proposed method demonstrates substantial potential for clinical applications, showcasing its capacity to provide accurate and efficient vertebral rotation angle measurements.

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Background

We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO).

Methods

This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary hospital. Patients diagnosed as TAO and healthy volunteers who were age- and gender-matched met the eligibility criteria for recruitment. Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learning-based analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples t-test.

Results

A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from -0.01 mm [95% limits of agreement (LoA): -0.64 to 0.63 mm] to 0.09 mm (LoA: -0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 vs. 2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 vs. 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 vs. 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 vs. 26.34±4.72 mm; P<0.001), and total scleral area (SA_TOTAL) (96.14±34.38 vs. 56.91±14.97 mm²; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes.

Conclusions

Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.

Proximal femur parameter measurement via improved PointNet+

BACKGROUND

Femoral morphological studies and parameter measurements play a crucial role in diagnosing hip joint disease, preoperative planning for total hip arthroplasty, and prosthesis design. Doctors usually perform parameter measurements manually in clinical practice, but it is time-consuming and labor-intensive. Moreover, the results rely heavily on the doctor's experience, and the repeatability is poor. Therefore, the accurate and automatic measurement methods of proximal femoral parameters are of great value.

METHOD

We collected 300 cases of clinical CT data of the femur. We introduced the adaptive function adjustment module to the neural network PointNet++ to strengthen the global feature extraction of the point cloud for improving the accuracy of femur segmentation. We used the improved PointNet++ network to segment the femur into three parts: femoral head, femoral neck, and femoral shaft. We evaluated the segmentation accracy using Dice Coefficient, MIoU, recall, and precision indicators. We achieved the automatic measurement of the proximal femoral parameters using the shape fitting algorithms, and compared the automatic and manual measurement results.

RESULTS

The Dice, MIoU, recall and precision indicator of the improved segmentation algorithm reached 98.05%, 96.55%, 96.63%, and 96.03%, respectively. The comparison between automatic and manual measurement results showed that the mean accuracies of all parameters were above 95%, the mean errors were less than 5 mm and 3°, and the ICC values were more than 0.8, indicating that the automatic measurement results were accurate.

CONCLUSION

Our improved PointNet++ network provided high-precision segmentation of the femur. We further completed automatic measurement of the femur parameters and verified its high accuracy. This method is of great value for the diagnosis and preoperative planning of hip diseases.

Automated measurement of the disc-fovea angle based on DeepLabv3

Purpose

To assess the value of automatic disc-fovea angle (DFA) measurement using the DeepLabv3+ segmentation model.

Methods

A total of 682 normal fundus image datasets were collected from the Eye Hospital of Nanjing Medical University. The following parts of the images were labeled and subsequently reviewed by ophthalmologists: optic disc center, macular center, optic disc area, and virtual macular area. A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area. Then, the coordinates of the optic disc center and macular center were obstained by using the minimum outer circle technique. Finally the DFA was calculated.

Results

In this study, 205 normal fundus images were used to test the model. The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively. The mean intersection over union (MIoU), mean pixel accuracy (MPA), average error in the center of the optic disc, and average error in the center of the virtual macula obstained by using DeepLabv3+ model was 94.77%, 97.32%, 10.94 pixels, and 13.44 pixels, respectively. The automatic DFA measurement using DeepLabv3+ got the less error than the errors that using the other segmentation models. Therefore, the DeepLabv3+ segmentation model was finally chosen to measure DFA automatically.

Conclusions

The DeepLabv3+ segmentation model -based automatic segmentation techniques can produce accurate and rapid DFA measurements.

Automatic measurement of vascular calcifications in patients with aorto-iliac occlusive disease to predict the risk of re-intervention after endovascular repair

OBJECTIVE

There is currently a lack of consensus and tools to easily measure vascular calcification using computed tomography angiography (CTA). The aim of this study was to develop a fully automatic software to measure calcifications and to evaluate the interest as predictive factor in patients with aorto-iliac occlusive disease.

METHODS

This study retrospectively included 171 patients who had endovascular repair of an aorto-iliac occlusive lesion at the University Hospital of Nice between January 2011 and December 2019. Calcifications volumes were measured from CT-angiography (CTA) using an automatic method consisting in 3 sequential steps: image pre-processing, lumen segmentation using expert system and deep learning algorithms and segmentation of calcifications. Calcification volumes were measured in the infrarenal abdominal aorta and the iliac arterial segments, corresponding to the common and the external iliac arteries.

RESULTS

Among 171 patients included with a mean age of 65 years, the revascularization was performed on the native external and internal iliac arteries in respectively: 83 patients (48.5%); 107 (62.3%) and 7 (4.1%). The mean volumes of calcifications were 2759 mm³ in the infrarenal abdominal aorta, 1821 mm³ and 1795 mm³ in the right and left iliac arteries. For a mean follow up of 39 months, TLR was performed in 55 patients (32.2%). These patients had higher volume of calcifications in the right and left iliac arteries, compared with patients who did not have a re-intervention (2274 mm³ vs 1606 mm³, p=0.0319 and 2278 vs 1567 mm³, p=0.0213).

CONCLUSION

The development of a fully automatic software would be useful to facilitate the measurement of vascular calcifications and possibly better inform the prognosis of patients.

Development of an Automatic Measurement Method for CD8 and PD-1 Positive T Cells Using Image Analysis Software

BACKGROUND/AIM

With the progress in cancer immunotherapy using immune checkpoint blockade (ICB) therapy, histological observations of tumor-infiltrating lymphocyte (TIL) status are needed to evaluate the antitumor effect of ICB using imaging analysis software.

MATERIALS AND METHODS

Formalin-fixed paraffin-embedded sections obtained from colorectal cancer and gastric cancer patients with more than 500 single nucleotide variants were stained with anti-CD8 and anti-PD-1 antibodies. Based on our own algorithm and imaging analysis software, an automatic TIL measurement method was established and compared to the manual counting methods.

RESULTS

In the CD8⁺ T cell number measurement, there was a good correlation (r=0.738 by Pearson test) between the manual and automated counting methods. However, in the PD-1⁺ T cell measurement, there was a large difference in TIL numbers in both groups. After adjustment of the parameter settings, the correlation between the manual and automated methods in the PD-1⁺ T cell measurements improved (r=0.668 by Pearson test).

CONCLUSION

An imaging software-based automatic measurement could be a simple and useful tool for evaluating the therapeutic effect of cancer immunotherapies in terms of TIL status.

Automatic measurement of axial vertebral rotation in 3D vertebral models

Axial Vertebral Rotation (AVR) is a significant indicator of adolescent idiopathic scoliosis (AIS). A host of methods are provided to measure AVR on coronal plane radiographs or 3D vertebral model. This paper provides a method of automatic AVR measurement in 3D vertebral model that is based on point cloud segmentation neural network and the tip of the spinous process searching algorithm. An improved PointNet using multi-input and attention mechanism named Multi-Input PointNet is proposed, which can segment the upper and lower endplates of the vertebral model accurately to determine the transverse plane of vertebral model. An algorithm is developed to search the tip of the spinous process according to the special structure of vertebrae. AVR angle is measured automatically using the midline of vertebral model and projection ofy-axis on the transverse plane of vertebral model based on points obtained above. We compare automatic measurement results with manual measurement results on different vertebral models. The experiment shows that automatic results can achieve accuracy of manual measurement results and the correlation coefficient of them is 0.986, proving our automatic AVR measurement method performs well.

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

Evaluation of a Fully Automatic Measurement of Short-Term Variability of Repolarization on Intracardiac Electrograms in the Chronic Atrioventricular Block Dog

Background: Short-term variability (STV) of repolarization of the monophasic action potential duration (MAPD) or activation recovery interval (ARI) on the intracardiac electrogram (EGM) increases abruptly prior to the occurrence of ventricular arrhythmias in the chronic AV-block (CAVB) dog model. Therefore, this parameter might be suitable for continuous monitoring of imminent arrhythmias using the EGM stored on an implanted device. However, 24/7 monitoring would require automatic STV_ARI measurement by the device.

Objective: To evaluate a newly developed automatic measurement of STV_ARI for prediction of dofetilide-induced torsade de pointes (TdP) arrhythmias in the CAVB-dog.

Methods: Two retrospective analyses were done on data from recently performed dog experiments. (1) In seven anesthetized CAVB-dogs, the new automatic STV_ARI method was compared with the gold standard STV_MAPD at baseline and after dofetilide administration (0.025 mg/kg in 5 min). (2) The predictive value of the automatic method was compared to currently used STV_ARI methods, i.e., slope method and fiducial segment averaging (FSA) method, in 11 inducible (≥3 TdP arrhythmias) and 10 non-inducible CAVB-dogs.

Results: (1) The automatic measurement of STV_ARI had good correlation with STV_MAPD (r² = 0.89; p < 0.001). Bland-Altman analysis showed a small bias of 0.06 ms with limits of agreement between −0.63 and 0.76 ms. (2) STV_ARI of all three methods was significantly different between inducible and non-inducible dogs after dofetilide. The automatic method showed the highest predictive performance with an area under the ROC-curve of 0.93, compared to 0.85 and 0.87 of the slope and FSA methods, respectively. With a threshold of STV set at 1.69 ms, STV_ARI measured with the automatic method had a sensitivity of 0.91 and specificity of 0.90 in differentiating inducible from non-inducible subjects.

Conclusion: We developed a fully-automatic method for measurement of STV_ARI on the intracardiac EGM that can accurately predict the occurrence of ventricular arrhythmias in the CAVB-dog. Future integration of this method into implantable devices could provide the opportunity for 24/7 monitoring of arrhythmic risk.

Automatic measurement of head-perineum distance during intrapartum ultrasound: description of the technique and preliminary results

OBJECTIVES

To evaluate the accuracy and reliability of a new ultrasound technique for the automatic assessment of the head-perineum distance (HPD) during childbirth.

METHODS

HPD was measured on a total of 40 acquisition sessions in 30 laboring women both automatically by an innovative algorithm and manually by trained sonographers, assumed as gold standard.

RESULTS

A significant correlation was found between manual and automatic measurements (Intra-CC = 0.994). High values of the coefficient of determination (r²=0.98) and low residual errors: RMSE = 2.01 mm (4.9%) were found.

CONCLUSION

The automatic algorithm for the assessment of the HPD represents a reliable technique.

Automated Measurement of Heart Girth for Pigs Using Two Kinect Depth Sensors

The heart girth parameter is an important indicator reflecting the growth and development of pigs that provides critical guidance for the optimization of healthy pig breeding. To overcome the heavy workloads and poor adaptability of traditional measurement methods currently used in pig breeding, this paper proposes an automated pig heart girth measurement method using two Kinect depth sensors. First, a two-view pig depth image acquisition platform is established for data collection; the two-view point clouds after preprocessing are registered and fused by feature-based improved 4-Point Congruent Set (4PCS) method. Second, the fused point cloud is pose-normalized, and the axillary contour is used to automatically extract the heart girth measurement point. Finally, this point is taken as the starting point to intercept the circumferential perpendicular to the ground from the pig point cloud, and the complete heart girth point cloud is obtained by mirror symmetry. The heart girth is measured along this point cloud using the shortest path method. Using the proposed method, experiments were conducted on two-view data from 26 live pigs. The results showed that the heart girth measurement absolute errors were all less than 4.19 cm, and the average relative error was 2.14%, which indicating a high accuracy and efficiency of this method.

Development of a Solar-Powered IoT-Based Instrument for Automatic Measurement of Water Clarity

Water clarity is the most common indicator of water quality. The purpose of the study was to develop an instrument which can automatically measure water clarity in place of manual measurement by Secchi disk. The instrument is suspended by buoys at the water surface and uses solar energy to measure the light intensity of LED bulbs after passing through a water column; the result is then converted to Secchi depth by using a regression function. Measurement data are stored in a cloud server so that mobile users can access via an Internet connection. Three experiments were conducted to examine the instrument performance: (i) to ensure light intensity of the LED bulbs is strong enough to pass through the water column; (ii) to determine the regression relationship between the measured light intensity of the instrument and Secchi depth; and (iii) to evaluate the coefficient of variation (CV) of the measured water clarity when using our instrument and a conventional Secchi disk. Experiment results show that the measured values of light intensity are stable with the average CV = 5.25%. Moreover, although there are slight differences between the Secchi depth measured by our instrument and those measured by Secchi disk, the measurements by our instrument can efficiently replace the measurements by conventional Secchi disk, which can be affected by weather conditions as well as by human subjectivity.

Atlas-based algorithm for automatic anatomical measurements in the knee

We present an algorithm for automatic anatomical measurements in tomographic datasets of the knee. The algorithm uses a set of atlases, each consisting of a knee image, surface segmentations of the bones, and locations of landmarks required by the anatomical metrics. A multistage volume-to-volume and surface-to-volume registration is performed to transfer the landmarks from the atlases to the target volume. Manual segmentation of the target volume is not required in this approach. Metrics were computed from the transferred landmarks of a best-matching atlas member (different for each bone), identified based on a mutual information criterion. Leave-one-out validation of the algorithm was performed on 24 scans of the knee obtained using extremity cone-beam computed tomography. Intraclass correlation (ICC) between the algorithm and the expert who generated atlas landmarks was above 0.95 for all metrics. This compares favorably to inter-reader ICC, which varied from 0.19 to 0.95, depending on the metric. Absolute agreement with the expert was also good, with median errors below 0.25 deg for measurements of tibial slope and static alignment, and below 0.2 mm for tibial tuberosity-trochlear groove distance and medial tibial depth. The automatic approach is anticipated to improve measurement workflow and mitigate the effects of operator experience and training on reliability of the metrics.

On-Barn Pig Weight Estimation Based on Body Measurements by Structure-from-Motion (SfM)

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized.

Change in Pelvic Sagittal Inclination From Supine to Standing Position Before Hip Arthroplasty

BACKGROUND

Cup anteversion and inclination are important for avoiding implant impingement and dislocation in total hip arthroplasty. However, functional cup anteversion and cup inclination also change as the pelvic sagittal inclination (PSI) changes. Therefore, PSI in both supine and standing positions was measured in a large cohort in this study.

METHODS

A total of 422 patients (median age, 61; range, 15-87) who underwent total hip arthroplasty were the subjects of this study. There were 83 patients with primary osteoarthritis (OA), 274 patients with developmental dysplasia-derived secondary OA, 48 patients with osteonecrosis, and 17 patients with rapidly destructive coxopathy (RDC). Preoperative PSI in supine and standing positions was measured by automated computed topography segmentation and landmark localization of the pelvis followed by intensity-based 2D-3D registration, and the number of cases in which PSI changed more than 10° posteriorly was calculated. Hip disease, sex, and age were analyzed if they were related to a PSI change of more than 10°.

RESULTS

The median PSI was 5.1° (interquartile range, 0.4°-9.4°) in supine and -1.3° (interquartile range, -6.5° to 4.2°) in standing position. There were 79 cases (19%) in which the PSI changed more than 10° posteriorly from supine to standing. Elder age and patients with primary OA and RDC were revealed to be the related factors.

CONCLUSION

PSI changed more than 10° posteriorly from supine to standing in 19% of cases. Age and diagnosis of primary OA and RDC were the significant factors for the posterior rotation.

Prick test: evolution towards automated reading

The prick test is one of the most common medical methods for diagnosing allergies, and it has been carried out in a similar and laborious manner over many decades. In an attempt to standardize the reading of the test, many researchers have tried to automate the process of measuring the allergic reactions found by developing systems and algorithms based on multiple technologies. This work reviews the techniques for automatic wheal measurement with the aim of pointing out their advantages and disadvantages and the progress in the field. Furthermore, it provides a classification scheme for the different technologies applied. The works discussed herein provide evidence that significant challenges still exist for the development of an automatic wheal measurement system that not only helps allergists in their medical practice but also allows for the standardization of the reading and data exchange. As such, the aim of the work was to serve as guideline for the development of a proper and feasible system.

Correlations between physiological parameters related with kidney function and minute-by-minute urine output

AIM

Recently, devices capable of measuring minute-by-minute urine output (UOm) have become available. It is not known how UOm correlates with physiological parameters in normal conditions and in disease states characterized by vascular dysfunction. This paper analyzes correlations between UOm and physiological parameters related to kidney perfusion to provide some insight about UOm pathophysiological interpretation and its relationship with renal blood flow.

METHODS

We studied 14 male pigs were anesthetized, tracheostomized, and mechanically ventilated. Mean systemic blood pressure (P_ART ), mean pulmonary artery blood pressure (P_PA ), carotid artery blood flow (Q_CA ), as well as total (Q_REN ), cortical (Q_COR ) and medullary (Q_MED ) renal blood flows, and the renal resistive index (RRI) were measured or calculated. Animals received an intravenous dose of live E. coli for the induction of sepsis (septic group), or an equivalent amount of normal saline (nonseptic group). Three groups were studied: nonseptic (n = 6) and septic (n = 4), both receiving for resuscitation NaCl 0.9% at 4 mL/kg per h; and septic (n = 4), receiving for resuscitation NaCl 0.9% at 17 mL/kg per h. Animals were monitored for 5 h after the induction of sepsis.

RESULTS

In septic animals, UOm was strongly positively correlated with Q_REN (Kendall's τ = 0.770, P < 0.05), Q_COR (τ = -0.566, P < 0.05) and Q_MED (τ = 0.632, P < 0.05); and negatively correlated with P_PA (τ = -0.524, P < 0.05) and RRI (τ = -0.672, P < 0.05). Control animals exhibited weaker correlations.

CONCLUSION

UOm is a good physiological surrogate marker of total and regional renal blood flows and vascular resistance, particularly under septic conditions, probably reflecting glomerulo-tubular dysfunction in sepsis.

Automatic polyp detection and measurement with computed tomographic colonography: A phantom study

Purpose

The purpose of this study is to assess the performance of computer-aided detection (CAD) software in detecting and measuring polyps for CT Colonography, based on an in vitro phantom study.

Material and methods

A colon phantom was constructed with a PVC pipe of 3.8 cm diameter. Nine simulated polyps of various sizes (3.2mm-25.4mm) were affixed inside the phantom that was placed in a water bath. The phantom was scanned on a 64-slice CT scanner with tube voltage of 120 kV and current of 205 mAs. Two separate scans were performed, with different slice thickness and reconstruction interval. The first scan (thin) had a slice thickness of 1mm and reconstruction interval 0.5mm. The second scan (thick) had a slice thickness of 2mm and reconstruction interval of 1mm. Images from both scans were processed using CT Colonography software that automatically segments the colon phantom and applies CAD that automatically highlights and provides the size (maximum and minimum diameters, volume) of each polyp. Two readers independently measured each polyp (two orthogonal diameters) using both 2D and 3D views. Readers’ manual measurements (diameters) and automatic measurements from CAD (diameters and volume) were compared to actual polyp sizes as measured by mechanical calipers.

Results

All polyps except the smallest (3.2mm) were detected by CAD. CAD achieved 100% sensitivity in detecting polyps ≥6mm. Mean errors in CAD automated volume measurements for thin and thick slice scans were 8.7% and 6.8%, respectively. Almost all CAD and manual readers’ 3D measurements overestimated the size of polyps to variable extent. Both over- and underestimation of polyp sizes were observed in the readers’ manual 2D measurements. Overall, Reader 1 (expert) had smaller mean error than Reader 2 (non-expert).

Conclusion

CAD provided accurate size measurements for all polyps, and results were comparable to the two readers' manual measurements