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

The Effectiveness of Virtual Reality in Managing Acute Pain and Anxiety for Medical Inpatients: Systematic Review.

BACKGROUND: Virtual reality is increasingly being utilized by clinicians to facilitate analgesia and anxiolysis within an inpatient setting. There is however, a lack of a clinically relevant review to guide its use for this purpose. OBJECTIVE: To systematically review the current evidence for the efficacy of virtual reality as an analgesic in the management of acute pain and anxiolysis in an inpatient setting. METHODS: A comprehensive search was conducted up to and including January 2019 on PubMed, Ovid Medline, EMBASE, and Cochrane Database of Systematic reviews according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Search terms included virtual reality, vr, and pain. Primary articles with a focus on acute pain in the clinical setting were considered for the review. Primary outcome measures included degree of analgesia afforded by virtual reality therapy, degree of anxiolysis afforded by virtual reality therapy, effect of virtual reality on physiological parameters, side effects precipitated by virtual reality, virtual reality content type, and type of equipment utilized. RESULTS: Eighteen studies were deemed eligible for inclusion in this systematic review; 67% (12/18) of studies demonstrated significant reductions in pain with the utilization of virtual reality; 44% (8/18) of studies assessed the effects of virtual reality on procedural anxiety, with 50% (4/8) of these demonstrating significant reductions; 28% (5/18) of studies screened for side effects with incidence rates of 0.5% to 8%; 39% (7/18) of studies evaluated the effects of virtual reality on autonomic arousal as a biomarker of pain, with 29% (2/7) demonstrating significant changes; 100% (18/18) of studies utilized a head mounted display to deliver virtual reality therapy, with 50% being in active form (participants interacting with the environment) and 50% being in passive form (participants observing the content only). CONCLUSIONS: Available evidence suggests that virtual reality therapy can be applied to facilitate analgesia for acute pain in a variety of inpatient settings. Its effects, however, are likely to vary by patient population and indication. This highlights the need for individualized pilot testing of virtual reality therapy's effects for each specific clinical use case rather than generalizing its use for the broad indication of facilitating analgesia. In addition, virtual reality therapy has the added potential of concurrently providing procedural anxiolysis, thereby improving patient experience and cooperation, while being associated with a low incidence of side effects (nausea, vomiting, eye strain, and dizziness). Furthermore, findings indicated a head mounted display should be utilized to deliver virtual reality therapy in a clinical setting with a slight preference for active over passive virtual reality for analgesia. There, however, appears to be insufficient evidence to substantiate the effect of virtual reality on autonomic arousal, and this should be considered at best to be for investigational uses, at present

Barriers to the Diffusion of Medical Technologies within Healthcare: A Systematic Review

Implementing medical devices into a clinical setting is a complex and lengthy process. Existing models, such as Rogers' Diffusion of Innovation, try to elucidate this process, however, need to be updated to the evolving healthcare context as fewer than 7% of devices achieve implementation. The aim of this systematic review was to describe the barriers to diffusion in relation to this model to understand why so few technologies are implemented and how to address these challenges to mitigate risk during the translation process. To do this, we searched PubMed, Medline, and Embase databases for studies published between 01/01/1999-30/10/2019. Theoretical and application studies were included, and thematic analysis was employed using the Braun and Clarke framework to generate broad themes from the specific concepts described by included studies. A total of 33 articles were eligible for inclusion. Innovation processes constituting an obstacle to diffusion included: technology-specific challenges (8/33), clinical evidence/uncertainty (5/33), regulatory affairs (6/33), health technology assessment (7/33), reimbursement (15/33), and adoption (6/33). The factors that contributed to these themes were identified as being associated to the 11 tenets of Rogers' Diffusion of Innovation. This allowed the discussion of the identified barriers to medical device diffusion in relation with the Rogers' model. This analysis enabled the development and proposal of a framework that incorporates considerations within commercialization and translation strategies for these barriers to ultimately facilitate medical technology implementation

A randomised controlled trial to assess the feasibility of utilising virtual reality to facilitate analgesia during external cephalic version.

External cephalic version (ECV) is associated with a moderate degree of pain. Virtual reality (VR) is a technology that has shown promise in offering procedural analgesia. We undertook a clinical pilot to assess the viability of VR to reduce pain during ECV. In an open randomised controlled trial (RCT), we randomised 50 women to either VR or standard care each (25 per group). Women receiving VR were administered VR content (Skylights) via a headset. Pre- and post-procedural measures of pain, anxiety, device experience and vital signs were measured. There were no significant differences between groups (VR/no VR) in pain scores (60.68 vs 49.76; p = 0.2), ECV success rates (80% vs 76%; p = 0.7) or anxiety levels. The women receiving VR had a significantly higher anticipation of pain pre-procedurally (70.0 vs 50.0; p = 0.03). 20 (80%) of the VR women indicated that they would use VR again and 22 (88%) indicated they would recommend it to a friend having ECV. There were no significant differences between groups for side effects encountered or changes in vital signs. We have shown that using VR during ECV is feasible and appears safe. Our results inform the design of future RCTs

Cholestasis in heart transplant recipients treated with cyclosporine.

Cyclosporine-A is being used increasingly for immunosuppression in patients undergoing organ transplantation. Hepatotoxicity has been noted to occur in up to 20% of renal transplant recipients receiving the drug. The exact nature of this hepato-toxicity, however, has not been defined, although it has been manifested principally, if not exclusively, as elevated serum bilirubin levels. The authors have observed a similar elevation of serum bilirubin levels in cardiac transplant recipients receiving cyclosporine-A and prednisone for immunosuppression