نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناس ارشد، مدیریت اطلاعات سلامت، دانشکده ی مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران
2 دانشیار، مدیریت اطلاعات بهداشتی درمانی، دانشکده ی مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران
چکیده
مقدمه: در سال‌های اخیر مفاهیم شبکه‌های عصبی مصنوعی در کشف اولیه و طبقه‌بندی بیماری‌ها متحمل پیشرفت‌های فراوانی شده است. استفاده از شبکه‌های عصبی به دلیل توانایی‌های بالقوه‌ی آن درکاربردهای پزشکی و در پیدا کردن کنش بین متغیرها، تشخیص و مدل‌سازی بیماری‌ها به طور وسیعی مقبول واقع شده است. هدف از این پژوهش، طراحی و پیاده‌سازی سیستم تصمیمیار مبتنی بر شبکه‌های عصبی مصنوعی به منظور کشف اولیه‌ی سرطان پروستات بود.
روش بررسی: پژوهش حاضر از نوع کاربردی و جامعه‌ی هدف آن متشکل از 360 بیمار مبتلا به ناهنجاری‌های پروستات بودند که در فواصل سال‌های 90-1388 به بخش اورولوژی بیمارستان امام خمینی (ره) شهر تهران مراجعه نمودند. در این پژوهش به منظور ارزیابی عملکرد سیستم طراحی شده، از شاخص‌های حساسیت، ویژگی و صحت در طبقه‌بندی استفاده گردید. در طراحی هسته‌ی محاسباتی سیستم تصمیمیار بالینی در کشف اولیه‌ی سرطان پروستات از بزرگی خوش‌خیم آن، از الگوریتم شبکه‌ی عصبی گرادیان توأم مدرج (Scaled conjugate gradient) استفاده شد.
یافته‌ها: شاخص‌های عملکردی این سیستم، ویژگی و حساسیت بودند و عملکرد سیستم تصمیمیار بالینی پیشنهاد شده بر اساس این شاخص‌ها به ترتیب عبارت از 06/97 و 11/92 درصد بود. نتایج سیستم تصمیمیار در تشخیص و طبقه‌بندی بیماری‌های نئوپلازی پروستات، حاکی از پتانسیل بالای سیستم‌های مبتنی بر شبکه‌های عصبی به عنوان ابزاری قوی در طبقه‌بندی ناهنجاری‌های پروستات بود.
نتیجه‌گیری: در این پژوهش یک سیستم تصمیمیار پزشکی با هدف یاری رساندن به متخصصین در تشخیص و طبقه‌بندی بیماری‌های نئوپلازی پروستات طراحی گردید. سیستم‌های هوشمند پزشکی بر مبنای هوش مصنوعی و به خصوص شبکه‌های عصبی، می‌توانند به پزشکان در تشخیص دقیق سرطان پروستات و بزرگی خوش‌خیم آن کمک نمایند. با استفاده از این سیستم‌ها، بیوپسی‌های غیر ضروری و هزینه‌های تشخیصی کاهش می‌یابد. به علاوه، این سیستم‌ها می‌توانند در به حداقل رساندن زمان فرایندهای تشخیصی بیماری‌ها مؤثر واقع شوند.
کلیدواژه‌ها
عنوان مقاله [English]
Designing a Clinical Decision Support System Based on Artificial Neural Network for Early Detection of Prostate Cancer and Differentiation from Benign Prostatic Hyperplasia *
نویسندگان [English]
- Mustafa Ghaderzadeh 1
- Farahnaz Sadoughi 2
- Arvin Ketabat 1
1 Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran
2 Associate Professor, Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran.
چکیده [English]
Introduction: In recent years, the concepts of artificial neural networks (ANN) have extensively
undergone remarkable development in early detection and classification of diseases such as
benign prostatic hyperplasia (BPH). The usage of ANN has become widely accepted in medical
applications owing to its potential capabilities for detecting the complex interactions among
variables, diagnosis and diseases’ modeling. The present study aimed to design and implement a
decision support system (DSS) based on ANN for early detection of prostate cancer.
Methods: This survey design was conducted through data collection among 360 males with
prostate abnormalities in Urology Department of Imam Khomeini Hospital, Tehran, Iran, from
January 2008 to March 2011. In order to assess the performance and accuracy of the designed
system, sensitivity, specificity and receiver-operating characteristics (ROC) curve were used as
the indicators of distinguishing prostate cancers from BPH. In order to implement DSS in this
study, scaled conjugate gradient (SCG) algorithm was used as the main algorithm for early
detection of prostate cancer from benign prostate.
Results: The proposed intelligent ANN-based system can be used as a strong diagnostic tool with
97.0% specificity and 92.1% sensitivity for detecting the prostate cancer and to differentiate it
from BPH. The results indicated a high potential of artificial neural network as a strong tool in
classification of prostatic neoplasia diseases.
Conclusion: A medical decision support system was used aiming to help medical experts in their
classification and early detection of prostatic neoplasia disorders in the present study. Such
artificial intelligent-based medical intelligent systems, particularly for neural networks, can help
physicians in accurate decision-making concerning prostate cancer and BPH. Using such
systems, specialists would be able to eliminate or minimize unnecessary biopsy and reduce
diagnostic costs. In addition, such systems can accelerate the diagnostic detection time.
کلیدواژه‌ها [English]
- Decision Support System
- Prostatic Neoplasia
- Artificial Neural Network
- Sensitivity
- Specificity
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