Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review

IntroductionPolycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.MethodsWe applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.Results135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).ConclusionArtificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022295287

Robotic Living Donor Right Hepatectomy: A Systematic Review and Meta-Analysis

The introduction of robotics in living donor liver transplantation has been revolutionary. We aimed to examine the safety of robotic living donor right hepatectomy (RLDRH) compared to open (ODRH) and laparoscopic (LADRH) approaches. A systematic review was carried out in Medline and six additional databases following PRISMA guidelines. Data on morbidity, postoperative liver function, and pain in donors and recipients were extracted from studies comparing RLDRH, ODRH, and LADRH published up to September 2020; PROSPERO (CRD42020214313). Dichotomous variables were pooled as risk ratios and continuous variables as weighted mean differences. Four studies with a total of 517 patients were included. In living donors, the postoperative total bilirubin level (MD: −0.7 95%CI −1.0, −0.4), length of hospital stay (MD: −0.8 95%CI −1.4, −0.3), Clavien–Dindo complications I–II (RR: 0.5 95%CI 0.2, 0.9), and pain score at day > 3 (MD: −0.6 95%CI −1.6, 0.4) were lower following RLDRH compared to ODRH. Furthermore, the pain score at day > 3 (MD: −0.4 95%CI −0.8, −0.09) was lower after RLDRH when compared to LADRH. In recipients, the postoperative AST level was lower (MD: −0.5 95%CI −0.9, −0.1) following RLDRH compared to ODRH. Moreover, the length of stay (MD: −6.4 95%CI −11.3, −1.5) was lower after RLDRH when compared to LADRH. In summary, we identified low- to unclear-quality evidence that RLDRH seems to be safe and feasible for adult living donor liver transplantation compared to the conventional approaches. No postoperative deaths were reported

Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study.

Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay

Trypanosomatids Detected in the Invasive Avian Parasite Philornis downsi (Diptera: Muscidae) in the Galapagos Islands

Alien insect species may present a multifaceted threat to ecosystems into which they are introduced. In addition to the direct damage they may cause, they may also bring novel diseases and parasites and/or have the capacity to vector microorganisms that are already established in the ecosystem and are causing harm. Damage caused by ectoparasitic larvae of the invasive fly, Philornis downsi (Dodge and Aitken) to nestlings of endemic birds in the Galapagos Islands is well documented, but nothing is known about whether this fly is itself associated with parasites or pathogens. In this study, diagnostic molecular methods indicated the presence of insect trypanosomatids in P. downsi; to our knowledge, this is the first record of insect trypanosomatids associated with Philornis species. Phylogenetic estimates and evolutionary distances indicate these species are most closely related to the Crithidia and Blastocrithidia genera, which are not currently reported in the Galapagos Islands. The prevalence of trypanosomatids indicates either P. downsi arrived with its own parasites or that it is a highly suitable host for trypanosomatids already found in the Galapagos Islands, or both. We recommend further studies to determine the origin of the trypanosomatid infections to better evaluate threats to endemic fauna of the Galapagos Islands

Population dynamics of an invasive bird parasite, Philornis downsi (Diptera: Muscidae), in the Galapagos Islands.

The invasive parasitic fly, Philornis downsi (Muscidae), is one of the greatest threats to the avifauna of the Galapagos Islands. The larvae of this fly feed on the blood and tissues of developing nestlings of at least 18 endemic and native birds. The aim of the current study was to investigate biotic and abiotic factors that may influence the population dynamics of this invasive parasite. To study the influence of vegetation zone and related climatic factors on fly numbers, a bi-weekly monitoring program using papaya-baited traps was carried out at a dry, lowland site and at a humid, highland site on Santa Cruz Island between 2012-2014. Female flies, a large proportion of which were inseminated and gravid, were collected throughout the year at both sites, indicating females were active during and between the bird breeding seasons. This is the first evidence that female flies are able to persist even when hosts are scarce. On the other hand, catch rates of male flies declined between bird breeding seasons. Overall, catch rates of P. downsi were higher in the drier, lowland habitat, which may be a consequence of host or resource availability. Time was a stronger predictor of adult fly numbers than climate, further suggesting that P. downsi does not appear to be limited by its environment, but rather by host availability. Seasonal catch rates suggested that populations in both habitats were continuous and multivoltine. Numbers of adult female flies appeared to be regulated chiefly by simple direct density dependence, and may be governed by availability of bird nests with nestlings. Nevertheless, confounding factors such as the existence of reservoir hosts that perpetuate fly populations and changes in behavior of P. downsi may increase the vulnerability of bird hosts that are already IUCN red-listed or in decline

Host-specific associations affect the microbiome of Philornis downsi, an introduced parasite to the Galápagos Islands

The composition and diversity of bacteria forming the microbiome of parasitic organisms have implications for differential host pathogenicity and host–parasite co‐evolutionary interactions. The microbiome of pathogens can therefore have consequences that are relevant for managing disease prevalence and impact on affected hosts. Here, we investigate the microbiome of an invasive parasitic fly Philornis downsi, recently introduced to the Galápagos Islands, where it poses extinction threat to Darwin's finches and other land birds. Larvae infest nests of Darwin's finches and consume blood and tissue of developing nestlings, and have severe mortality impacts. Using 16s rRNA sequencing data, we characterize the bacterial microbiota associated with P. downsi adults and larvae sourced from four finch host species, inhabiting two islands and representing two ecologically distinct groups. We show that larval and adult microbiomes are dominated by the phyla Proteobacteria and Firmicutes, which significantly differ between life stages in their distributions. Additionally, bacterial community structure significantly differed between larvae retrieved from strictly insectivorous warbler finches (Certhidea olivacea) and those parasitizing hosts with broader dietary preferences (ground and tree finches, Geospiza and Camarhynchus spp., respectively). Finally, we found no spatial effects on the larval microbiome, as larvae feeding on the same host (ground finches) harboured similar microbiomes across islands. Our results suggest that the microbiome of P. downsi changes during its development, according to dietary composition or nutritional needs, and is significantly affected by host‐related factors during the larval stage. Unravelling the ecological significance of bacteria for this parasite will contribute to the development of novel, effective control strategies