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Artificial Intelligence - Springer Nature

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Last Updated: 10 January 2023

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Artificial Intelligence for Pre-operative Diagnosis of Malignant Thyroid Nodules Based on Sonographic Features and Cytology Category

Background The diagnosis and classification of thyroid nodules are highly dependent on personal preference. Patients of thyroid nodules between 2010 and 2020 were recruited from our institution's database into training and testing groups. The training group USG images were reviewed by a study radiologist who worked in thyroid USG, who provided the key characteristics and supplemented with statistics obtained from existing studies to minimize sampling error. We designed four AI models based on classification algorithms and evaluated their diagnostic results of thyroid malignancy. Support vector machine classifier was found to do the best in forecasting final histopathology with an accuracy of 89%, sensitivity 89%, specificity 83%, F-score 94%, and AUROC 0. 86. Conclusions We've created a first of its kind, pilot AI model that can accurately predict malignancy in thyroid nodules using USG attributes, FNAC, demographics, and serum TSH.

Source link: https://doi.org/10.1007/s00268-022-06798-1


Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world

Nine residents and three senior radiologists were invited to make a u201d or u201cmalignant diagnosis based on camera images of the United States without knowing the AI results. Conclusions The AI system's results for cancer diagnosis were comparable to that of a senior thyroid radiologist, according to the author. U2022 The AI system attained a senior radiologist-like degree in thyroid cancer diagnosis and could greatly enhance resident diagnostic results. U2022 The AI-assisted approach greatly enhanced u2264 1. 5 cm thyroid cancer screening AUC, quality, and sensitivity of the patients, contributing to an increased detection of thyroid cancer but maintaining a similar consistency to that of radiologists alone, which made it more effective than radiologists alone. u2022 (1996) The AI-assisted approach greatly reduced the unnecessary biopsy rate for thyroid nodules > 1. 5 cm by the residents, while keeping a similar sensitivity to radiologists alone, with the same sensitivity to that of radiologists.

Source link: https://doi.org/10.1007/s00330-022-09378-y


Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence is a computer science that can imitate human behavior by performing intelligent analysis of data. A large amount of medical data is generated every day by those who live in the digital age. Machine learning can help analyze, and analyze the data used in healthcare services. We introduce several models and general processes of machine learning, as well as their involvement in pharmacological science in this article. Therefore, deep learning and machine learning AI with deep learning may be useful in pharmaceutical research.

Source link: https://doi.org/10.1007/s43440-022-00445-1


Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence

In addition, we explore how the NN can reveal further material insights that are otherwise unobtainable from a black-box model by using the SHapley Additive exPlanations explainable artificial intelligence software. We note that high Al and Cr content forms the protective oxide layer, while Mo in FeCrAl creates a thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion.

Source link: https://doi.org/10.1557/s43579-022-00315-0


AI as a challenge for legal regulation – the scope of application of the artificial intelligence act proposal

The AI Act's proposal is the first comprehensive attempt to properly regulate AI. The selection of the term as Artificial Intelligence Act raises legal and legislative concerns regarding concept development as a legal strategy and legislative tool. Using the example of the Artificial Intelligence Act, this article explores the difficulties of governing the concept of AI.

Source link: https://doi.org/10.1007/s12027-022-00725-6


Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles

In patients undergoing single thawed embryo transfer cycles, the purpose is to investigate the value of standardised preimplantation genetic testing for aneuploidy using artificial intelligence. PGT-A was used in a single, large university-based fertility center with patients undergoing in vitro fertilization from February 2015 to April 2020, according to the study's Retrospective cohort. Overall, those tested with AI 1. 0 showed an elevated euploidy rate, reduced simple mosaicism rate, and reduced aneuploidy rate. Overall, those tested via AI 2. 0 revealed a significant rise in euploidy rate and reduced simple mosaicism rate. When comparing AI 2. 0 to NGS, the aneuploidy rate was marginally reduced. The OP/LBR ratio in the AI 2. 0 group was significantly higher than the OP/LBR group. The BPR was significantly lower in the AI 2. 0 group, according to the AI 2. 0 group. Conclusion Standardized PGT-A via AI improves euploidy classification rates and OP/LBR by a significant margin, and reduces BPR when compared to conventional NGS.

Source link: https://doi.org/10.1007/s10815-022-02695-7


Artificial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L.

Ceratophyllum demersum L. u2014, a well-known floating macrophyte, can be used to phytoremediate heavy metals and other pollutants in aquatic environments. In vitro regenerated plants of C. demersum were exposed to 24, 72, and 120 h to 0, 0. 5, 1. 0, 2. 0, 2. 0, and 4. 0 mg/L of cadmium in water, with 24. 72, and 120 h to 0, 0. 5, 1. 0, 2. 0, 2. 0, and 4. 0 mg/L of cadmium in water. Cd uptake by plants, Cd uptake by plants, bioconcentration factor, and Cd removal from water were all found to have a significant relationship, according to the results, which indicated a significant difference in terms of Cd in water, Cd uptake by plants, Cd uptake by plants, Cd uptake by plants, Cd uptake by plants, Cd uptake by plants, Cd uptake by plants, Cd removal from water. With exposure time, Cd uptake by plants and BCF values increased sharply. BCF values in plant samples exposed to 2 mg/L Cd for 72 hours were found for plant samples exposed to 2 mg/L Cd for 72 hours. These published findings show that in vitro regenerated C. demersum can be used for phytoremediation of Cd-contaminated aquatic habitats.

Source link: https://doi.org/10.1007/s11356-022-25081-3

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions