Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices

Evidence-based recommendations of hemato-oncological guidelines: quality assessment and the role of industry-sponsored trials

Guidelines in clinical practice play a fundamental role in applying evidence-based medicine or professional guidance to clinical practice. An increasing financial conflict of interest in clinical trials in general medicine has been illustrated in recent literature. Pharmaceutical-funded clinical drug trials yield positive outcomes for company products more frequently than independent trials do. In this line, we aimed to identify whether there is a role of conflict of interest (COls) in the hemato-oncology field. Thus, we searched hemato-oncological guidelines (April 1st, 2007 and March 31st, 2017) from the selected transnational societies by the experts in the field of hemato-oncology. Clinical practice guidelines (CPGS) and consensus statements complying with the inclusion and exclusion criteria were included in the study and analysed the proportion of reported clinical trials funded by industry and non-industry for each guideline. Quality assessments were performed with the Appraisal of Guidelines for Research and Evaluation II (AGREE-11) tool. We identified 110 guidelines, of which 57 were excluded; 53 guidelines included were developed by 7 transnational societies. Overall, we identified 927 treatment recommendations made by 507 trial citations, of which 255 (50.3%) were industry and 252 (49.7%) non-industry sponsored. The AGREE-ll overall assessment score was less for specialised oncology developers (33.5%) than general guideline developers (52.8%). Of those six AGREE-Il domains, the applicability domain scored (19.8%) less for the oncology specialised concerning general guideline developers (41.0%). Concluding, we identified that the guidelines produced by ESMO, ELN and NCCN societies are driven to make recommendations by a greater proportion of industry-sponsored trials. The very low-quality score is reported in the guidelines established by the ELN, ESMO and NCCN society. Whereas AHS and BSH, medium-quality scores are registered. While the guidelines developed by CCO and NICE societies, higher quality scores are registered

Benefit sharing and globalisation of industry sponsored clinical trials for breast cancer research DOI: 10.20517 / 2394-4722.2018.108. In JOURNAL OF CANCER METASTASIS AND TREATMENT - ISSN: 2394-4722

Introduction: The burden of cancer is the greatest and rising most rapidly in low-income and middle-income countries (LMICs). The survival of breast cancer is especially poor in LMICs due to late stage presentation and inadequate access to therapy. Whereas low-income countries (LICs) suffer from a generalized lack of access to cancer care, in middle-income countries (MICs) such services and facilities may exist. However, highly-priced innovative medicines are often only affordable for certain subsets of the population, and good outcomes remain biased toward those who can pay. We carried out comprehensive analysis of the geographic distribution of breast cancer clinical trials involving at least one LMICs clinical site and trials evaluating costly innovative medicines. Methodology: Data were extracted from clinicaltrials.gov registry (as of 30 Jun 2018). Advanced search filters used: (1) condition/disease: breast cancer; (2) study type: Interventional studies; (3) phases: I to IV; (4) funder type: industry. Countries were classified by income according to the World Bank (Fiscal year 2019). Results: We analysed 1,746 trials. The fraction of phase III trials involving MICs sites was 55.36% (155/280) in which Lower-MICs (L-MICs) were 27.14% (76/280) and Upper-MICs (UMICs) were 54.64% (153/280). Smaller proportions of Phase I and II trials were conducted in MICs i.e. 5.92% (26/439) and 16.23% (161/992). Phase IV trials that involve MICs were 31.43% (11/35). No trials were conducted in LICs. L-MICs countries with the highest number of trials were India (n = 63) and Ukraine (n = 56) followed by Philippines (n = 23), Egypt (n = 17), and Pakistan (n = 10). For U MICs, Russian Federation (n = 141), Brazil (n = 113), China (n = 112), followed by Mexico (n = 88) and Turkey (n = 66). Conclusion: Our analysis of the case of industry sponsored clinical trials with new, innovative medicines for breast cancer confirms previous observations with rarer blood cancers, that such trials are increasingly globalized, i.e., delocalized to countries that do not have the means to ensure (universal) access to high-cost medicines. Although substantial numbers of anti-cancer medicines are nowadays included in national lists of LMICs, their affordability and accessibility at country-level are often far from ensured. The relevance of benefit sharing in international research is still poorly understood. A legal framework formulating benefitsharing requirements in international research is necessar

Libro universitario: Letteratura, medicina e scienze sociali. Convergenze tra culture e linguaggi; Chapter- Per un’etica della condivisione dei benefici in tema di sperimentazione dei farmaci

'Che le “due culture” abbiano bisogno l’una dell’altra è, nel pensiero di alcuni, affermazione, invero, che rasenta quasi la banalità. E la netta constatazione appare comprensibile se si considera che ad assumerla è uno studioso che la riferisce in modo particolare al mondo antico, nel quale la profonda influenza del pensiero greco ha generato riflessioni prive di soluzioni di continuità tra le due sfere del sapere: «da Aristotele a Posidonio a Seneca la conoscenza viene concepita e, nei limiti del possibile, praticata come totale, il che non vuol dire che ci furono allora soltanto temperamenti leonardeschi ma che – al di là degli ovvi limiti sogget-tivi – l’unità del sapere era un dato generalmente acquisito» (L. Canfora).

AI in SARS-CoV-2 outbreak

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve

Platform-Based Robot-Assisted Rehabilitation

The use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. The Virtual Reality (VR)-based Rutgers Ankle and the Hunova were found to be the effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries

FRONTLINE THERAPY FOR NON-TRANSPLANT ELIGIBLE MULTIPLE MYELOMA: A CRITICAL APPRAISAL OF PUBLISHED NETWORK META-ANALYSES

Newly diagnosed multiple myeloma (MM) who are transplant ineligible (NTE NDMM) are usually treated with multiple-drug combinations including proteasome inhibitors, immunomodulatory drugs and alkylating agents. Recently approved combo therapies including anti-CD38 monoclonal agents and/or lenalidomide improve progression-free survival (PFS) as compared with one of the standard treatments. We thus aimed at assessing the relative efficacy of novel daratumumab-based and lenalidomide based triplets/quadruplets as compared with overall standard treatments for NTE NDMM, namely Rd and VMP. Network meta-analyses (NMA) are accepted evidence-based tools for conducting indirect comparisons among treatments, however, the scientific community is still skeptical regarding their robustness. We, therefore conducted an umbrella review: fully published NMAs were retrieved by standard searches (EBMASE, Cochrane Library, MEDLINE/PubMed) and appraised by AMSTAR-2 and ROBIS tools. Three indirect comparisons of PFS were targeted: 1) VRD versus VMP, 2) DaraRd versus VMP, 3) DaraVMP versus Rd. Overall 17 NMA addressing NDMM were published since Jan 2017: 6 fully published ones including both daratumumab- and lenalidomide-based novel treatments were appraised. The overall quality of the NMAs was poor to moderate according to AMSTAR-2 and ROBIS. Each NMA analyzed 6 to 27 trials and 2 ones were company sponsored. 1) VRD was compared to VMP by 3 moderate-quality NMAs, which consistently reported a significant amelioration of PFS or higher SUCRA of VRD, while OS-HR was not conclusive. 2) DaraRd was compared to VMP by 4 NMAs and the pooled PFSHR ranged from 0.39 to 0.61. A significant amelioration of OS was also reported by the unique NMA assessing this endpoint. 3) DaraVMP was compared versus Rd by 4 NMAs. Pooled HR ranged from 0.35 to 0.71, which was statistically significant in two ones. DaraVMP achieved the highest SUCRA (0.960) in the latest and largest NMA (Giri et al 2020). Only one NMA compared OS of the two regimens and did not report a significant advantage of DaraVMP. In conclusion, Dara-VMP, VRD and Dara-Rd show mostly a favorable PFS profile as both directly and indirectly compared with standard frontline treatments for NTE NDMM. NMAs are valuable evidence-based tools, however, their quality needs to be appraised before using their result to support clinical recommendations. Future NMAs are expected to incorporate also safety endpoints in order to allow benefit to risk assessments

Access to medicine among asylum seekers, refugees and undocumented migrants across the migratory cycle: a scoping review protocol

Introduction: Migration creates new health vulnerabilities and exacerbates pre-existing medical conditions. Migrants often face legal, system-related, administrative, language and financial barriers to healthcare, but little is known about factors that specifically influence migrants' access to medicines and vaccines. This scoping review aims to map existing evidence on access to essential medicines and vaccines among asylum seekers, refugees and undocumented migrants who aim to reach Europe. We will consolidate existing information and analyse the barriers that limit access at the different stages of the migratory phases, as well as policies and practices undertaken to address them. Methods: We follow the Arksey and O'Malley framework for knowledge synthesis of research, as updated by Levac et al. For reporting the results of our search and to synthetise evidence, we will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extended reporting guideline for scoping reviews. This scoping review consists of five iterative stages. Bibliographic databases (PubMed, CINAHL, Cochrane Database of Systematic Reviews and Scopus) and grey literature databases (Open Grey, Grey Literature Report and Google Scholar, Web of Science Conference Proceedings, non-governmental organisations and United Nations agency websites) will be searched for relevant studies. Dissemination and ethics: This review will be disseminated through a peer-reviewed article in a scientific open-access journal and conference presentations. Furthermore, findings will be shared at workshops of research and operational stakeholders for facilitating translation into research and operational practices. Since it consists of reviewing and collecting data from publicly available materials, this scoping review does not require ethics approval

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic