Manual Estimation Research Articles

Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images.

Ultrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound images relies on experience, and intra- and interrater reliability may vary among studies. In this study, two types of convolutional neural networks (CNNs), U-Net and SegNet, were used to extract the median nerve morphology. To the best of our knowledge, the application of these methods to ultrasound imaging of the median nerve has not yet been investigated. Spearman's correlation and Bland-Altman analyses were performed to investigate the correlation and agreement between manual annotation and CNN estimation, namely, the cross-sectional area, circumference, and diameter of the median nerve. The results showed that the intersection over union (IoU) of U-Net (0.717) was greater than that of SegNet (0.625). A few images in SegNet had an IoU below 0.6, decreasing the average IoU. In both models, the IoU decreased when the median nerve was elongated longitudinally with a blurred outline. The Bland-Altman analysis revealed that, in general, both the U-Net- and SegNet-estimated measurements showed 95% limits of agreement with manual annotation. These results show that these CNN models are promising tools for median nerve ultrasound imaging analysis.

Latest Trend and Challenges in Machine Learning– and Deep Learning–Based Computational Techniques in Poultry Health and Disease Management: A Review

To determine the flock’s economic worth, free‐range chicken growers must determine the gender, bird movement, behavior, disease detection, and lameness of the chickens. However, because of the complex environmental background and the fluctuating chicken population, it is difficult for farmers to effectively and properly measure those characteristics. Manual estimation is also inaccurate and time‐consuming because probable identification occurs in their life cycle. Therefore, the industry benefits from automated systems that can produce findings quickly and precisely in managing health and diseases. The advancement of machine learning (ML)– and deep learning (DL)–based algorithms are boons for poultry health and disease management. This study reviews the literature using ML and DL techniques in prediction, classification, and disease detection in various metrics, namely, poultry health and disease management. We have considered the research article published from 2010 to 2023 in this study, which uses ML‐ and DL‐based computation techniques in poultry welfare metrics such as gender identification, tracking of poultry, analysis of broiler chicken behavior, detection of poultry diseases, lameness and broiler weight, and stress monitoring. In addition, this review explores the most recent developments, difficulties, strategies, and databases used in image preprocessing feature extraction and classification. The review addresses these challenges and discusses the approaches and techniques researchers employ to tackle them in the field of poultry management and disease detection.

G2P2C — A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes

Developing diagnostic and treatment solutions for medical applications is often challenging due to the complex dynamics, partial observability, high inter- and intra-population variability, and the presence of unknown delays and disturbances. A characteristic case is the control of glucose concentration in people with Type 1 Diabetes (T1D) through the administration of exogenous insulin. The above complexities, enhanced by the significant cognitive burden associated with the estimation of optimal insulin dosing related to daily activities such as food intake and exercise, call for advanced insulin administration solutions towards a fully automated Artificial Pancreas System (APS). Reinforcement Learning (RL) is currently being explored in the development of APSs thanks to its demonstrated potential in problems characterized by complex dynamics and uncertainties. Despite the progress, RL algorithms in T1D still require manual estimation and announcement of meal carbohydrate (CHO) content or rely on small meal scenarios. In this study, we proposed G2P2C, a modular deep RL algorithm, which aims to fully automate glucose control in T1D, eliminating the need for CHO estimation and announcement. G2P2C was designed based on the state-of-the-art Proximal Policy Optimization (PPO) algorithm, augmented by two novel optimization phases: (i) model learning and (ii) planning. The former integrated an auxiliary learning task to learn a glucose dynamics model. The latter fine-tuned the learned control strategy to a short-time horizon by simulating glucose trajectories into the future. We evaluated the performance of G2P2C in-silico on a challenging meal protocol (180 g of CHO per day) for 20 subjects (10 adults and 10 adolescents) using an open-source version of a T1D simulator approved by the United States Food and Drug Administration (FDA). G2P2C was compared with state-of-the-art RL algorithms and two basal-bolus (BB) clinical treatment strategies, which involve manual meal announcement and CHO estimation with automated correction insulin boli for elevated glucose. G2P2C obtained statistically significant (P<0.05) reward improvements compared to PPO in 18 out of 20 subjects, while maintaining a lower failure rate. In addition, G2P2C achieved a time in range of 73% and 64% for the adult and adolescent cohorts, respectively, outperforming BB strategies in the adult cohort although no meal announcement was performed. The control performance and algorithmic characteristics of G2P2C show promise as a candidate algorithm for glucose control in APSs. We released the codebase of G2P2C (https://github.com/chirathyh/G2P2C) and an online demonstration tool (https://capsml.com/), where users can perform custom simulations to compare G2P2C with BB strategies, under the MIT license.

Estimation of economic life and feasibility of combine harvesters in Bangladesh deploying a unique web-based app

Combine harvester is one of the most crucial pieces of harvesting equipment in modern agriculture. Farmers and entrepreneurs employing a combine harvester need to focus on two pivotal factors: its profitability and optimal replacement time. Proper performance in this scenario can lead to prompt, and high-quality farm operations, enhancing income and simultaneously reducing product expenses significantly. This paper proposes a web-based application that would allow estimating the economic life and feasibility of combine harvesters in Bangladesh. The minimum cost value determination process has been used to estimate economic life by applying the double declining depreciation method. A high-level Python web framework has been used for the design and development of the software. The combine harvester exhibits a consistent economic life of 10 years for 300 to 450 hrs of annual use, with an increase in average yearly use from 300 to 450 hrs. As average yearly usage increases, the combine harvester's economic life declines drastically after 500 hrs of operation. Comparison with manual estimation shows that economic feasibility estimated by the web-based app provides more relevant data. The web-based app provides a useful tool for farmers, and entrepreneurs in Bangladesh to estimate the economic life and feasibility of combine harvesters, enabling better decision-making and improved managerial policies.

Reduced perception-action dissociation in children with amblyopia

The functional distinction between vision-for-perception and vision-for-action is a key aspect of understanding the primate visual system. While this dissociation has been well-established in adulthood, its development and dependence on typical visual experience remain unclear. To address these questions, we examined two groups of children: typically developed children and those with amblyopia, who presumably have a sub-optimal visual experience. The Ponzo illusion, known to impact perception but not visuomotor behaviors across age groups, was employed to assess the extent of dissociation. Participants engaged in two tasks involving the Ponzo illusion: a grasping task (vision-for-action) and a manual estimation task (vision-for-perception), with objects placed on the "close" and "far" surfaces of the illusion. Typically developed children displayed grasping movements that were unaffected by the illusion, as their grasping apertures were scaled based on object size, independent of its location.In contrast, children with amblyopia exhibited a clear susceptibility to the illusion, showing larger apertures for objects placed on the 'far' surface of the illusion, and smaller apertures for objects placed on the 'close' surface. Interestingly, both groups of children demonstrated similar susceptibility to the illusion during the perceptual task, with objects placed on the far surface being perceived as longer compared to objects placed on the close surface. These findings shed light on the impact of atypical visual development on the emergence of the dissociation between perception and action, highlighting the crucial role of typical visual experience in establishing this distinction.

ShinyFruit: interactive fruit phenotyping software and its application in blackberry.

Horticultural plant breeding programs often demand large volumes of phenotypic data to capture visual variation in quality of harvested products. Increasing the throughput potential of phenomic pipelines enables breeders to consider data-hungry molecular breeding strategies such as genome-wide association studies and genomic selection. We present an R-based web application called ShinyFruit for image-based phenotyping of size, shape, and color-related qualities in fruits and vegetables. Here, we have demonstrated one potential application for ShinyFruit by comparing its estimates of fruit length, width, and red drupelet reversion (RDR) with ImageJ and analogous manual phenotyping techniques in a population of blackberry cultivars and breeding selections from the University of Arkansas System Division of Agriculture Fruit Breeding Program. ShinyFruit results shared a strong positive correlation with manual measurements for blackberry length (r = 0.96) and ImageJ estimates of RDR (r = 0.96) and significant, albeit weaker, correlations with manual RDR estimation methods (r = 0.62 - 0.70). Neither phenotyping method detected genotypic differences in blackberry fruit width, suggesting that this trait is unlikely to be heritable in the population observed. It is likely that implementing a treatment to promote RDR expression in future studies might strengthen the documented correlation between phenotyping methods by maximizing genotypic variance. Even so, our analysis has suggested that ShinyFruit provides a viable, open-source solution to efficient phenotyping of size and color in blackberry fruit. The ability for users to adjust analysis settings should also extend its utility to a wide range of fruits and vegetables.

Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study

BackgroundThe commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI). ObjectivesTo test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit. MethodsDuring a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an “exact match,” “far match,” or an “intrusion” using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland–Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods. ResultsExact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland–Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit’s Nutrition AI, manual estimation, and overall app. ConclusionOpenfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit’s usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585.

Urologist validation of an artificial intelligence-based tool for automated estimation of penile curvature

Severity of penile curvature (PC) is commonly used to select the optimal surgical intervention for hypospadias, either alone or in conjunction with other phenotypic characteristics. Despite this, current literature on the accuracy and precision of different PC measurement techniques in hypospadias patients remains limited. Assess the feasibility and validity of an artificial intelligence (AI)-based model for automatic measurement of PC. Seven 3D-printed penile models with variable degrees of ventral PC were used to evaluate and compare interobserver agreement in estimation of penile curvatures using various measurement techniques (including visual inspection, goniometer, manual estimation via a mobile application, and an AI-based angle estimation app. In addition, each participant was required to complete a questionnaire about their background and experience. Thirty-five clinical practitioners participated in the study, including pediatric urologists, pediatric surgeons, and urologists. For each PC assessment method, time required, mean absolute error (MAE), and inter-rater agreement were assessed. For goniometer-based measurement, the lowest MAE achieved was derived from a model featuring 86° PC. When using either UVI (unaid visual inspection), mobile apps, or AI-based measurement, MAE was lowest when assessing a model with 88° PC, indicating that high-grade cases can be quantified more reliably. Indeed, MAE was highest when PC angle ranged between 40° and 58° for all the investigated measurement tools. In fact, among these methodologies, AI-based assessment achieved the lowest MAE and highest level of inter-class correlation, with an average measurement time of only 22s. AI-based PC measurement models are more practical and consistent than the alternative curvature assessment tools already available. The AI method described in this study could help surgeons and hypospadiology researchers to measure PC more accurately.

Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet.

Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox–Gastaut syndrome

ObjectivesGeneralized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox–Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection of GPFA has been more challenging, due to its variability from patient to patient and similarity to normal brain rhythms. In this work, a deep learning model is investigated for detection of GPFA events and estimating their overall burden from scalp EEG. MethodsData from 10 patients recorded during four ambulatory EEG monitoring sessions are used to generate and validate the model. All patients had confirmed LGS and were recruited into a trial for thalamic deep-brain stimulation therapy (ESTEL Trial). ResultsThe correlation coefficient between manual and model estimates of event counts was r2 = 0.87, and for total burden was r2 = 0.91. The average GPFA detection sensitivity was 0.876, with an average false-positive rate of 3.35 per minute. There was no significant difference found between patients with early or delayed deep brain stimulation (DBS) treatment, or those with active vagal nerve stimulation (VNS). ConclusionsOverall, the deep learning model was able to accurately detect GPFA and provide accurate estimates of the overall GPFA burden and electrographic event counts, albeit with a high false-positive rate. SignificanceAutomated GPFA detection may enable automated calculation of EEG biomarkers of burden of disease in LGS.

ESTIMATION AND COMPARISON OF PLATELET COUNT: AUTOMATED ANALYZERVS MANUAL PERIPHERAL SMEAR METHOD

Accurately assessing platelet counts holds paramount importance in the realm of diagnostics and treatment. The adoption of automated methods within the diagnostic landscape is steadily growing, attributed to their multifaceted benefits. Nonetheless, automated analyzers sometimes yield erroneous results, particularly in scenarios involving particles of comparable sizes or instances of light scattering. These encompass fragmented red blood cells (RBCs), microcytic RBCs, and the presence of oversized platelets or platelet clusters. In response, the manual estimation of platelet counts using Leishmans stain validation gains significance. Method: this study encompassed the collection of blood samples from a diverse pool of 100 patients, including both inpatients and outpatients, spanning the period from 1st February 2023 to 31st July 2023. These samples, procured from GMC Doda and its affiliated hospital, were collected in ethylenediaminetetraacetic acid (EDTA) tubes. The manual platelet count was done, followed by a comprehensive comparative analysis with the counts obtained through automated platelet analysis. Results: the outcomes unveiled a nuanced trend, with manual slide-derived results slightly surpassing those generated by automated analyzers. Conclusion: This study concluded a discernible correlation between automated and manual methodologies for platelet count assessment. Nevertheless, in instances marked by exceedingly high or low platelet counts, the manual method holds its ground, ensuring precision by circumventing concerns like platelet clumping or uneven distribution.

Automated Cell Counting of Macrophages In Situ.

Using the open-source image analysis software CellProfiler to automatically quantify antibody-stained or fluorescently labeled macrophages in situ provides accurate and reproducible cell counts. It is a vastly enhanced alternative method to both manual cell counting and estimation of cell marker expression based on fluorescence intensity. Quantification of tissue-resident macrophages acquired on widefield or confocal microscopes can be batch processed using our pipeline to produce data within minutes.

Methods for automated delineation and assessment of EMG responses evoked by peripheral nerve stimulation in diagnostic and closed-loop therapeutic applications

Objective. Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments. Approach. Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions’ recruitment curves (92 curves across all participants) and on the conditioning protocol’s subsequent baseline trials (∼1200 per participant) performed near H max. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later. Main results. Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicit H max fell within of the retrospective estimate; for the ‘threshold’ current that produced an M-wave 10% of maximum, this value was . Significance. Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.

HARNESSING PHOTOGRAMMETRY FOR WHITEWATER SLALOM COURSES IN NATURAL RIVERBEDS: AN INNOVATIVE APPROACH FOR COURSE DESIGN AND ANALYSIS

Abstract. Whitewater slalom, a thrilling and challenging water sport, requires carefully designed courses that provide a dynamic and engaging experience for athletes. Traditional methods of designing slalom courses in natural riverbeds have relied on manual measurements and estimations, which can be time-consuming, labour-intensive, and subject to human error. However, with the advent of photogrammetry, a cutting-edge technology that involves the use of aerial or ground-based cameras to capture and process 3D data from images, there is a new frontier in designing and analysing white water slalom courses.This article explores the usage of photogrammetry in the context of white-water slalom course design in natural riverbeds. It highlights how photogrammetry can revolutionize the course design process by providing accurate and detailed 3D models of the riverbed terrain, which can be used for virtual simulations, rapid prototyping, and precise analysis of different design options. The advantages of using photogrammetry include improved accuracy, efficiency, and cost-effectiveness in comparison to traditional methods.The article concludes by discussing the challenges and limitations of using photogrammetry for whitewater slalom course design, including issues related to data acquisition, processing, and accuracy. Despite these challenges, the potential of photogrammetry in revolutionizing the design and analysis of white-water slalom courses in natural riverbeds is immense, and it opens up exciting opportunities for further research, innovation, and advancement in the field of water sports.

A regressive encoder-decoder-based deep attention model for segmentation of fetal head in 2D-ultrasound images

Ultrasound imaging is the most commonly used imaging during pregnancy for tracking the fetus's growth and monitoring other biological parameters. The assessment of the development of the baby's growth requires imaging-based analysis in every trimester. The automatic computerized software and systems provide the platform for radiologists to more accurately access the fetus's head circumference as compared to manual estimation. The improvement of such computerized algorithms is always the key demand to improve accuracy and precision. This paper proposes an improved encoder-decoder model for the segmentation of the fetal head segmentation in 2D-ultrasound images. The proposed model uses regression in combination with attention to the encoder-decoder model to determine the fetus's head circumference. The model is further extended with the post-processing ellipse fitting to superimpose the segmentation region on ultrasound images for clear visualization of the fetus's head. Further, the proposed model performance is evaluated by using various statistical measures using segmented regions and available ground truth. The experimental results demonstrate a similarity score of 94.56%. The comparative result suggests that the proposed model is providing a more accurate fetus head segmentation region on 2D-ultrasound images as compared to other existing approaches.

PO-04-095 ACCURATE EAM-BASED TARGET DELINEATION IN STEREOTACTIC ARRHYTHMIA RADIOABLATION OF VT

Stereotactic Arrhythmia Radiotherapy (STAR) aided by precise image-guided delivery and respiratory motion management has been used successfully for experimental treatment of refractory VT. However, accurate target selection remains challenging. Current methods often rely on manual delineation and/or visual estimation of predefined cardiac segments. Develop a systematic process for registering VT targets from electroanatomic maps (EAM) to reference imaging coordinates of the planning CT (pCT) used for radiation treatment. The goal is to minimize potential variations in target site selection as well as treated volumes. In patients on antiarrhythmic treatment and undergoing a 2nd unsuccessful VT ablation, EA maps of the LV (and LA/RV when possible) and more focal maps of VT target sites were acquired (CARTO®3, Biosense-Webster, CA, USA) and exported. In-house software was used to register these to a planning image set and convert them to DICOM structure files (Fig 1). Dice similarity coefficients were calculated between ventricular mesh data and corresponding reference contours. Initial targets (GTV) were expanded to include internal target volumes (ITV) and planning target volumes (PTV) to account for respiratory and cardiac motion and residual uncertainties, with minor manual adjustments guided by consensus among electrophysiologists, radiation oncologists and medical physicists. STAR was delivered as a single fraction of 25 Gy using volumetric-modulated arc therapy or dynamic conformal arc therapy depending on the target shape. 7 patients with refractory VT were treated by defining targets based on registering individualized EAM maps on the planning images. Dice similarity indices between transferred reference map and reference contours were 0.83 ± 0.04 and 0.72 ± 0.04 for LV and LA/RV, respectively (table 1). The median clinical target was 22cc, with PTVs ranging from 62.4 to 199.6 cc. All patients demonstrated a significant reduction in VT events by 6 weeks post STAR. We have developed an effective methodology for direct registration of VT targets selected for STAR, based on direct anatomic registration of individualized EA maps acquired during ablation procedures. This process offers a way to efficiently delineate variable target boundaries in a semi-automated way, which is advantageous when unusual contours make standard segmentation inaccurate and visual estimation difficult. However, rigorous comparison with other approaches remains to be done.Tabled 1Patient-specific metrics1234567Dice, LV0.8680.8450.7910.8060.7210.8720.794Dice, LA/RV0.7520.6670.734N/A0.382N/A0.338GTV (cc)22.313.526.520.759.863.927.2ITV (cc)45.529.142.230.959.8127.829.6PTV (cc)98.662.488.275.5166.8199.672.8 Open table in a new tab

Analysing Maize Plant Height Using Unmanned Aerial Vehicle (UAV) RGB based on Digital Surface Models (DSM)

Maize (Zea mays L.) is one of the essential agricultural products in Papua Province of Indonesia, specifically in the three largest maize producing regions, namely Nabire Regency, Biak Numfor Regency and Merauke Regency, with the number of productions of 991 tons, 764 tons, and 751 tons respectively in 2015. Unfortunately, since 2016 the secondary data on food crops productivity, including maize, has not been provided yet in the provinces statistical report, due to manual estimation methods, i.e., visual estimation. On the other side, the number of populations in this Province has a slight increase, from 2.97 million people in 2012 to 3.38 million in 2019. Further, approximately 1.20 million people are employed in the agricultural sector. Considerable population growth will intensify the demand for food stock and other utilization of food crops in this region; hence, relevant research in food crops needs to be considered. One of the dominant factors in the yield potential of maize is plant height, since it is associated with fertilizer, seed, and soil treatment and predicts yield area. Therefore, this study aims to analyse the plant height, particularly maize plant based on a digital surface model (DSM) derived from Unmanned Aerial Vehicle (UAV) Red Green Blue (RGB) images. The crop was monitored during the second and third week of January 2022 and then, processed using pix4d Mapper software to produce the DSM, Digital Terrain Model (DTM), and orthomosaic. Then, the Geographical Information System (GIS) software, and an open-source software, namely Python were used to estimate the plant height. Next, the results were assessed statistically to examine the validation, the strong correlation coefficient of the estimation to the actual height that obtained from UAV and ground-based plant height data. The findings will help to support the prior decision support on estimation of maize production in Papua Province.

Third Ventricle Width is a Reliable and Clinically-Feasible Marker of Brain Atrophy in the Multiple Sclerosis Clinical Care Setting (P10-3.003)

<h3>Objective:</h3> Examine reliability and clinical utility of third ventricle width (TVW) on standard-of-care MRIs as a marker of cerebral atrophy in real-world multiple sclerosis (MS) clinical care settings. <h3>Background:</h3> Cerebral atrophy is emerging as an important marker of MS disease progression, but clinically-feasible volumetric measurements remain elusive in clinical care settings. Research supports TVW as a reliable, valid, fast, and easy manual two-dimensional MRI estimate of cerebral atrophy that correlates with clinical disability and tracks with volumetric changes. Importantly, most TVW research has utilized standardized research MRIs; it is unclear how findings translate to standard-of-care (SoC) MRIs (i.e., variable field strengths and sequence parameters). We evaluated real-world clinical utility by examining reliability and clinical relevance of TVW measured from SoC (i.e., clinical) MRIs. <h3>Design/Methods:</h3> We measured TVW from SoC brain MRIs in 563 consecutive patients aged 18–65 years. Three trained raters blind to other patient data measured TVW in millimeters from midpoints of left and right boundaries of the third ventricle in axial T1-weighted images. TVWs were adjusted for demographic characteristics. Disability was assessed with the gold-standard MS Functional Composite (MSFC) evaluating cognition (SDMT), upper extremity coordination (NHPT), and gait (T25FW). GLM assessed TVW differences across level of level of impairment (number of MSFC components &lt;5^th percentile). <h3>Results:</h3> Interrater reliability of TVW measurement indicated excellent absolute-agreement (two-way mixed effects ICCs: single-rating 0.982; 95%CI: 0.958, 0.994; mean-rating <i>k</i>=3, 0.994; 0.986, 0.998). Medium-to-large difference in TVW across MSFC impairment (<i>p</i> for trend &lt;0.001; η_p²=0.089): TVW (mean [95%CI]) increased across patients impaired on no (n=245; 3.34 [3.14, 3.54]), one (n=155; 3.74 [3.45, 3.99]), two (n=106; 4.30 [4.00, 4.60]), and three (n=57; 4.81 [4.40, 5.22]) MSFC tasks. <h3>Conclusions:</h3> Measurement of TVW from standard-of-care MRIs is a highly-reliable and clinically-relevant marker of brain atrophy in MS that can be easily implemented into routine clinical care. <b>Disclosure:</b> Dr. Levy has nothing to disclose. Dr. Patel has nothing to disclose. Mr. Sumowski has nothing to disclose.

Abstract 6112: Automated scoring to assess RAD51-mediated homologous recombination in patient-derived tumor organoids of ovarian cancers

Abstract Extensive trials of PARP inhibitors (PARPi) have shown that they improve progression-free survival in patients with various cancers, including ovarian cancers. This improvement is marked in patients carrying BRCA mutations, and in Homologous Recombination Deficiency (HRD) subgroups. To date, identifying patients eligible for PARPi has been a challenge for scientific and clinical teams using next-generation sequencing (NGS) analysis and the persistence of genomic scars in tumors after restoration of proficient HR or epigenetic changes is a limitation of NGS. Functional assays could be used to improve the profiling of HR status and faithfully identify HRD tumors. The Repair Capacity Test assesses the formation of RAD51 foci in proliferating cells after irradiation and can be used on fresh primary cancer tissues irradiated ex vivo as well as on Patient-Derived Tumor Organoids (PDTO). However, RAD51 foci scoring is often performed manually without any possibility for the standardization of techniques. By contrast, recent progress in whole slide imaging referring to scan a complete microscope slide and creating a single high-resolution digital file, could represent an opportunity for automatizing the evaluation of HR. The purpose of this translational study was to develop an automated tool for scoring RAD51-mediated homologous recombination, to use this tool on ovarian PDTO and to compare the result to the sensitivity to olaparib (PARPi), determined by direct exposure of PDTO to the drug. We show that immunofluorescence of RAD51 foci can be automatically detected and quantified in Cyclin A2 positive nuclei in all the cells of each PDTO slice thus offering a new opportunity for the routine management and standardization of HR assessment in PDTO that goes beyond the widely used manual estimation. Finally, this automated scoring will be optimized to directly assess HR on tumor slices to make the Repair Capacity Test a highly relevant functional precision oncology tool to identify patients who will benefit from PARPi. Citation Format: Lucie Thorel, Pierre-Marie Morice, Nicolas Elie, Louis-Bastien Weiswald, Romane Florent, Florence Giffard, Margaux Jacobs, Agathe Ricou, Raphaël Leman, Guillaume Babin, Jean-François Lebrun, Sandrine Martin, Mélanie Briand, Benoit Goudergues, Bernard Lambert, Cécile Blanc-Fournier, Dominique Vaur, Benoit Plancoulaine, Laurent Poulain. Automated scoring to assess RAD51-mediated homologous recombination in patient-derived tumor organoids of ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6112.

Comparison of Linear Regression, Neural Net, and Arima Methods For Sales Prediction of Instrumentation and Control Products In PT. Sarana Instrument

PT. Sarana Instrument is a national private company which is an authorized sales agent for several instrumentation and control products originating from European countries and the United States for sales in Indonesia. Every company certainly targets sales to be achieved every year, for that the company certainly needs sales forecasting. PT. Sarana Instrument does not currently have a prediction system so that for making annual sales targets, it still uses manual estimates by looking at sales data from the previous year's sales. So that PT. Sarana Instrument cannot get accurate sales predictions and the company cannot prepare human resources and financial resources according to the company's needs. Therefore, a forecasting system is needed to help make forecasts. The purpose of this study was to analyze the error rate of forecasting sales data for 2013-2021 at PT. Sarana Instrument uses a forecasting algorithm, namely the Linear Regression Algorithm, Neural Net and Arima, in order to obtain a sales forecast method with the smallest error rate and can be implemented at PT. PT. Sarana Instrument.