Embryo selection through artificial intelligence versus embryologists: a systematic review

STUDY QUESTION What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? SUMMARY ANSWER AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. WHAT IS KNOWN ALREADY The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. STUDY DESIGN, SIZE, DURATION The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: (‘Artificial intelligence’ OR ‘Machine Learning’ OR ‘Deep learning’ OR ‘Neural network’) AND (‘IVF’ OR ‘in vitro fertili*’ OR ‘assisted reproductive techn*’ OR ‘embryo’), where the character ‘*’ refers the search engine to include any auto completion of the search term. PARTICIPANTS/MATERIALS, SETTING, METHODS A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. MAIN RESULTS AND THE ROLE OF CHANCE Twenty articles were included in this review. There was no specific embryo assessment day across the studies—Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist’s visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59–94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists’ assessment following local respective guidelines. Using blind test datasets, the embryologists’ accuracy prediction was 65.4% (range 47–75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68–90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58–76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67–98%), while clinical embryologists had a median accuracy of 51% (range 43–59%). LIMITATIONS, REASONS FOR CAUTION The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. WIDER IMPLICATIONS OF THE FINDINGS AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers’ perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. STUDY FUNDING/COMPETING INTERESTS This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. REGISTRATION NUMBER CRD4202125633

Online UAV path planning for joint detection and tracking of multiple radio-tagged objects

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, and Damith C. Ranasingh

Distributed multi-object tracking under limited field of view sensors

We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm . To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel label consensus approach that reduces label inconsistency caused by objects’ movements from one node’s limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution’s real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios.Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Damith C. Ranasingh

Joint learning of set cardinality and state distribution

We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural language processing and other related problems. Despite their success, traditional architectures suffer from a serious limitation in that they are built to deal with structured input and output data, i.e. vectors or matrices. Many real-world problems, however, are naturally described as sets, rather than vectors. Existing techniques that allow for sequential data, such as recurrent neural networks, typically heavily depend on the input and output order and do not guarantee a valid solution. Here, we derive in a principled way, a mathematical formulation for set prediction where the output is permutation invariant. In particular, our approach jointly learns both the cardinality and the state distribution of the target set. We demonstrate the validity of our method on the task of multi-label image classification and achieve a new state of the art on the PASCAL VOC and MS COCO datasets.S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi, Anthony Dick, Ian Rei

LAVAPilot: lightweight UAV trajectory planner with situational awareness for embedded autonomy to track and locate radio-tags

Tracking and locating radio-tagged wildlife is a labor-intensive and time-consuming task necessary in wildlife conservation. In this article, we focus on the problem of achieving embedded autonomy for a resource-limited aerial robot for the task capable of avoiding undesirable disturbances to wildlife. We employ a lightweight sensor system capable of simultaneous (noisy) measurements of radio signal strength information from multiple tags for estimating object locations. We formulate a new lightweight task-based trajectory planning method-LAVAPilot-with a greedy evaluation strategy and a void functional formulation to achieve situational awareness to maintain a safe distance from objects of interest. Conceptually, we embed our intuition of moving closer to reduce the uncertainty of measurements into LAVAPilot instead of employing a computationally intensive information gain based planning strategy. We employ LAVAPilot and the sensor to build a lightweight aerial robot platform with fully embedded autonomy for jointly tracking and planning to track and locate multiple VHF radio collar tags used by conservation biologists. Using extensive Monte Carlo simulation-based experiments, implementations on a single board compute module, and field experiments using an aerial robot platform with multiple VHF radio collar tags, we evaluate our joint planning and tracking algorithms. Further, we compare our method with other information-based planning methods with and without situational awareness to demonstrate the effectiveness of our robot executing LAVAPilot. Our experiments demonstrate that LAVAPilot significantly reduces (by 98.5%) the computational cost of planning to enable real-time planning decisions whilst achieving similar localization accuracy of objects compared to information gain based planning methods, albeit taking a slightly longer time to complete a mission. To support research in the field, and conservation biology, we also open source the complete project. In particular, to the best of our knowledge, this is the first demonstration of a fully autonomous aerial robot system where trajectory planning and tracking to survey and locate multiple radio-tagged objects are achieved onboard.Hoa Van Nguyen, Fei Chen, Joshua Chesser, Hamid Rezatofighi, Damith Ranasingh

Online multi-target tracking using recurrent neural networks

We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindle

Investigation of virulence factors and their relationship with antimicrobial resistance among uropathogenic Escherichia coli isolates identified from patients in Basrah city, Iraq

<p>Uropathogenic Escherichia coli (UPEC) is one of the main causes of urinary tract infections (UTIs). We aimed to investigate the antimicrobial resistance (AMR) pattern, the frequency of some virulence genes (VGs), and the association of AMR with VGs.</p><p>A total of 300 urine samples were collected from patients suspected to have UTI. The samples were examined by biochemical and microbiological methods and VITEK2 compact system to identify the bacterial infectious agents. The antimicrobial resistance pattern and virulence genes (papAH, papC, papEF, papG, fimH, and fyuA) profile of UPEC isolates were investigated and the relationship between these traits was evaluated by statistical methods.</p><p>Among these samples, 201 (67%) exhibited a positive growth on culture media. E. coli was isolated from 60 (29.85 %) specimens followed by Klebsiella pneumoniae 42 (20.90%), Staphylococcus aureus 38 (18.9%), Enterobacter spp 29 (14.43%), Pseudomonas aeruginosa 10 (4.98%), Proteus mirabilis 15 (7.46%), others about 7 (3.48 %) isolates. Antibiogram results of 15 antibiotics examined showed that all E. coli isolates were multidrug-resistant (MDR). The commonest antimicrobial resistance was observed against Streptomycin (100%), Kanamycin (98.3%), and Ampicillin (96.7%). The most sensitive agents were Meropenem (96.4%), Nitrofurantoin (93.4%), and Imipenem (85%). VGs detected among UPEC isolates were fimH (88.3%), papAH (85%), papC (85%), papG (80%), fyuA (80%), and papEF (60%).These results alleged no strong correlation between VGs and AMR in E. coli strains.</p><p>Based on the results of the present study, virulence genes, and antimicrobial resistance are independent properties and can transfer to other bacteria separately.Further studies are needed to better understand the relationship between different virulence factors (VFs) and AMR at a molecular level, as most UPEC isolates express several VFs and AMR simultaneously.</p&gt

Autonomous UAV sensor system for searching and locating VHF radio-tagged wildlife

We consider the problem of tracking and localizing radio-tagged targets, a labor-intensive and time-consuming task necessary for wildlife conservation fieldwork. We design a lightweight sensor system for measurement of radio signal strength information from multiple radio tags. The sensor system is designed to suit low-cost, versatile, easy to operate multi-rotor UAVs. In this demo paper, we demonstrate our Unmanned Aerial Vehicle (UAV) sensor system for tracking and locating multiple VHF radio tags.Hoa Van Nguyen, Michael Chesser, Fei Chen, S. Hamid Rezatofighi, and Damith C. Ranasingh