scholarly journals Using artificial intelligence to predict the intrauterine insemination success rate among infertile couples

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Azadeh Akbari Sene ◽  
Zahra Zandieh ◽  
Mojgan Soflaei ◽  
Hamid Mokhtari Torshizi ◽  
Kourosh Sheibani
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Success Rate ◽  
Predictive Factors ◽  
Treatment Cycle ◽  
Intrauterine Insemination ◽  
Progressive Motility ◽  
Positive Pregnancy Test ◽  
Infertile Couples ◽  
Neural Network Input

Abstract Background To evaluate the use of artificial intelligence (AI) in predicting the success rate of intrauterine insemination (IUI) treatment among infertile couples and also to determine the importance of each of the parameters affecting IUI success. This study was a retrospective cohort study in which information from 380 infertile couples undergoing IUI treatment (190 cases resulting in positive pregnancy test and 190 cases of failed IUI) including underlying factors, female factors, sperm parameters at the beginning of the treatment cycle, and fertility results were collected from 2013 to 2019 and evaluated to determine the effectiveness of AI in predicting IUI success. Results We used the most important factors influencing the success of IUI as a neural network input. With the help of a three-layer neural network, the accuracy of the AI to predict the success rate of IUI was 71.92% and the sensitivity and specificity were 76.19% and 66.67%, respectively. The effect of each of the predictive factors was obtained by calculating the ROC curve and determining the cut-off point. Conclusions The morphology, total motility, and progressive motility of the sperm were found to be the most important predictive factors for IUI success. In this study, we concluded that by predicting IUI success rate, artificial intelligence can help clinicians choose individualized treatment for infertile couples and to shorten the time to pregnancy.

scholarly journals AI Feynman: A physics-inspired method for symbolic regression

2020 ◽  
Vol 6 (16) ◽  
pp. eaay2631 ◽  
Author(s):  
Silviu-Marian Udrescu ◽  
Max Tegmark
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Success Rate ◽  
Unknown Function ◽  
State Of The Art ◽  
Practical Interest ◽  
Symbolic Regression ◽  
The State ◽  
Np Hard ◽  
Test Set

A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.

scholarly journals Effects of Sperm Parameters on Pregnancy Rate in Patients Undergoing Intrauterine Insemination

2020 ◽  
pp. 1
Author(s):  
Mehmet Solakhan ◽  
Mustafa Demir
Keyword(s):  
Intrauterine Insemination ◽  
Sperm Count ◽  
Sperm Concentration ◽  
Sperm Parameters ◽  
Motile Sperm ◽  
Progressive Motility ◽  
Age Related ◽  
Significant Difference ◽  
Sperm Volume ◽  
Infertile Couples

<p><strong>OBJECTIVE:</strong> In this study, the effects of sperm parameters on the success of intrauterine insemination were investigated. </p><p><strong>STUDY DESIGN:</strong> The data from 309 infertile couples who were admitted between 2012-2018 without a female factor were analyzed retrospectively and included in the study. After the administration of gonadotropin and hCG (5000-10000 IU), single insemination was performed in 36-40 hours in all cycles. All couples underwent routine infertility screening. The relationship between sperm parameters (motility, morphology, sperm count), patient age, duration of infertility with intrauterine insemination success was evaluated.</p><p><strong>RESULTS:</strong> There was no statistically significant difference between the two groups in terms of mean age and age related-parity. There was no statistically significant difference between male ages, liquefaction, and sperm volumes between the two groups (p=0.898, p=0.448, p=0.651). Before washing; There was a statistically significant difference between the sperm concentration, percentage of total motile sperm, percentage of progressive motility sperm, percentage of normal sperm morphology, and total sperm count between the two groups (p=0.0001, p=0.0001, p=0.0001, p=0.0001, p=0.0001). After sperm washing; the results were similar to those obtained before washing. While statistically significant difference was observed between sperm volume and sperm concentrations (p=0.023, p=0.018), no significant difference was observed between the two groups in total sperm count (p=0.612).</p><p><strong>CONCLUSION:</strong> As a result, during the application of intrauterine insemination to infertile couples, total motile sperm count, progressive motility sperm count ratio and high sperm ratio with normal morphology used in order to increase pregnancy success can be considered as criteria that increase the chances of success.</p>

The Application of Wavelet Neural Network in Prediction of the Fish Price

2014 ◽  
Vol 687-691 ◽  
pp. 1945-1949
Author(s):  
Hong Wei Li ◽  
Xiao Xiang Gao ◽  
Ke Jun Cheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Success Rate ◽  
Bp Algorithm ◽  
Price Forecasting ◽  
Bp Network ◽  
Market Prices ◽  
Short Period ◽  
Artificial Intelligence Technology

The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision

scholarly journals Estimation Methods for Infertility Treatment Success: Comparison of Four Methods

2021 ◽  
Author(s):  
Alireza Zarinara ◽  
Koorosh Kamali ◽  
Mohammad Mahdi Akhondi
Keyword(s):  
Survival Analysis ◽  
Success Rate ◽  
Life Table ◽  
Conditional Probability ◽  
Live Birth ◽  
Treatment Cycle ◽  
Treatment Success ◽  
Infertility Treatment ◽  
Estimation Methods ◽  
Infertile Couples

Objective: To analyze and compare four methods for estimating the chance of treatment success in infertile couples. Materials and methods: In a retrospective cohort study, information on demographic and clinical features, including age, body mass index (BMI), duration of infertility, semen analysis, previous history of treatment and clinical examination of infertile couples were analyzed. Treatment success (childbearing) was calculated with four methods as live birth ratio, conditional probability and survival analysis (life table and Kaplan-Meyer method) and results are compared. Results: The fertility ratio for the first treatment cycle was 29.72% which decreased to 23.13% by total treatment cycles. The success rate was 75.4%. With conditional probability calculation at the end of the five treatment cycles. With the life table method in a five-year period, the probability for live birth was 78% and by Kaplan-Meyer method 73.1% and the median of treatment time was 562 days. Conclusion: Calculation of infertility treatment success rate by only simple live birth ratio of childbearing couples is associated with underestimation. Using the conditional probability method reduces that underestimation, but it is not considered the censored cases in the treatments. It seems life table (as a proxy of survival analysis) presents the closest estimation to clinical facts with considering the repetition of the treatment cycle and the duration of treatment.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)

2019 ◽  
Vol 17 (1) ◽  
pp. 69-76
Author(s):  
Mohammad Shiddiq Ghozali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Object Detection ◽  
Open Source ◽  
Automated Teller Machine

Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat CCTV akan tetapi penjahat menggunakan alat bantu untuk menutupi wajahnya seperti helm, topi, masker dan kacamata hitam. Biasanya didepan pintu masuk ATM terpampang larangan untuk tidak menggunakan helm, topi, masker dan kacamata hitam serta tidak membawa rokok. Akan tetapi larangan itu masih tetap ada yang melanggar, dikarenakan tidak ada tindak lanjut ketika seseorang menggunakan benda-benda yang dilarang dibawa kedalam ATM. Oleh karena itu penulis membuat sistem pendeteksi obyek di bidang Kecerdasan Buatan untuk mendeteksi benda-benda yang dilarang digunakan ketika berada di ATM. Salah satu metode yang digunakan untuk menciptakan Object Detection yaitu You Only Look Once (YOLO). Implementasi ide ini tersedia pada DARKNET (open source neural network). Cara kerja YOLO yaitu dengan melihat seluruh gambar sekali, kemudian melewati jaringan saraf sekali langsung mendeteksi object yang ada. Oleh karena itu disebut You Only Look Once (YOLO). Pada penelitian ini, penulis membuat sistem yang masih dalam bentuk pengembangan, sehingga menjalankannya masih menggunakan command prompt. Keywords : Automated Teller Machine (ATM), Kecerdasan Buatan, Pendeteksi Obyek, You Only Look Once (YOLO)  

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

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