An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study.

BACKGROUND: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Photocurable and Temperature-Sensitive Bioadhesive Hydrogels for Sutureless Sealing of Full-Thickness Corneal Wounds.

Penetrating corneal wounds can cause severe vision impairment and require prompt intervention to restore globe integrity and minimize the risk of infection. Tissue adhesives have emerged as a promising alternative to suturing for mitigating postoperative complications. However, conventional water-soluble adhesives suffer formidable challenges in sealing penetrating corneal wounds due to dilution or loss in a moist environment. Inspired by the robust adhesion of mussels in aquatic conditions, an injectable photocurable bioadhesive hydrogel (referred to as F20HD5) composed of polyether F127 diacrylate and dopamine-modified hyaluronic acid methacrylate is developed for sutureless closure of corneal full-thickness wounds. F20HD5 exhibits high transparency, wound-sealing ability, proper viscosity, biodegradability, and excellent biocompatibility. It allows in situ cross-linking via visible light, thereby providing sufficient mechanical strength and adhesiveness. In vivo, the adhesive hydrogel effectively closed penetrating linear corneal incisions and corneal injuries with minimal tissue loss in rabbits. During the 56-day follow-up, the hydrogel facilitates the repair of the injured corneas, resulting in more symmetrical curvatures and less scarring in distinction to the untreated control. Thus, bioinspired hydrogel holds promise as an effective adhesive for sealing full-thickness corneal wounds.

Engineered Mesenchymal Stromal Cell Exosomes-Loaded Microneedles Improve Corneal Healing after Chemical Injury.

Corneal alkali burns represent a prevalent ophthalmic emergency with the potential to induce blindness. The main contributing mechanisms include excessive inflammation and delayed wound healing. Existing clinical therapies have limitations, promoting the exploration of alternative methods that offer improved efficacy and reduced side effects. Adipose-derived stem cell-exosome (ADSC-Exo) has the potential to sustain immune homeostasis and facilitate tissue regeneration. Nevertheless, natural ADSC-Exo lacks disease specificity and exhibits limited bioavailability on the ocular surface. In this study, we conjugated antitumor necrosis factor-α antibodies (aT) to the surface of ADSC-Exo using matrix metalloproteinase-cleavable peptide chains to create engineered aT-Exo with synergistic effects. In both in vivo and in vitro assessments, aT-Exo demonstrated superior efficacy in mitigating corneal injuries compared to aT alone, unmodified exosomes, or aT simply mixed with exosomes. The cleavable conjugation of aT-Exo notably enhanced wound healing and alleviated inflammation more effectively. Simultaneously, we developed poly(vinyl alcohol) microneedles (MNs) for precise and sustained exosome delivery. The in vivo results showcased the superior therapeutic efficiency of MNs compared with conventional topical administration and subconjunctival injection. Therefore, the bioactive nanodrugs-loaded MNs treatment presents a promising strategy for addressing ocular surface diseases.

Effective Treatment for Recurrent Ovarian Cancer Guided by Drug Sensitivity from Ascites-Derived Organoid: A Case Report.

So far, ovarian cancer has still been the most lethal gynecological malignancy. The chemotherapy and targeted medication are the mainstay for the recurrent ovarian cancer treatment. About 70% of the advanced-stage cases will relapse. Ascites-derived organoid is a pre-clinical model for the precise prediction of the therapeutic effectiveness for the ovarian cancer: it can be used to assess the drug sensitivity, to guide individualized precise treatment, and to improve advanced stage as well as recurrent ovarian cancer patient' survival and prognosis. Until now, there has been no report concerning the establishment of the organoid out of the patient's ascites and the concurrent usage of drug sensitivity test to guide the individualized precise treatment for the ovarian cancer. Here, we report a case of recurrent ovarian cancer of a 59-year-old female patient whose CA125 at its peak increased to 4523.4 U/mL. Then, patient's own ovarian cancer organoid was constructed from the ascites by the abdominocentesis; concurrently, medication sensitivity test was performed on the organoid to guide individualized precise treatment. After the treatment, CA125 decreased to 33.7 U/mL, and the patient's condition relieved effectively. This is the first published case report using ascites-derived organoid and the drug sensitivity test thereof to guide the precise treatment of recurrent ovarian cancer.

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review.

BACKGROUND: Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. OBJECTIVE: This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. METHODS: We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. RESULTS: A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. CONCLUSIONS: Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians' experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.