Semen evaluation methodology is complex and difficult to standardize. Rigorously standardized laboratory protocols and strict quality control (QC) are essential for meaningful comparison of data from multiple sites. We describe the methods used for determination of semen volume, sperm concentration, and percent sperm motility in the Study for Future Families, a multicenter study of semen quality in the United States. Each of these 3 semen parameters was assessed using 2 techniques, which provided the opportunity to compare precision and assess suitability for multicenter studies. Detailed protocols were used, and technicians were centrally trained. A total of 509 semen evaluations were performed. Semen volume measured by weight was greater (P <.0001) than that determined by pipetting (3.7 +/- 1.6 mL vs 3.2 +/- 1.6 mL). Sperm concentration determined using hemacytometer chambers was consistently higher (P <.001) than that using disposable MicroCell chambers (81.0 x 10(6)/mL vs 65.9 x 10(6)/mL). Precision was slightly greater for the MicroCell chamber. The percentage of motile sperm was assessed by a simple counting technique as well as by the World Health Organization categorical method that assigns individual motile sperm to "a," "b," and "c" categories on the basis of progression. When these 3 categories were collapsed, the methods provided values that were not statistically different (P >.05), although the collapsed values tended to be higher (58.1% vs 51.6%) and less precise (CV 7.7% vs 4.1%) for the categorical method than for motility determined using the simple method. The data obtained in this study demonstrate the critical need for rigorous standardization of protocols and techniques for multicenter studies.
Dempster-Shafer evidence theory has wide applications in many fields. Recently, A new entropy called Deng entropy was proposed in evidence theory. Some scholars have pointed out that Deng Entropy does not satisfy the additivity in uncertain measurements. However, irreducibility may have a huge effect. The derived entropy from complex systems is often irreducible. Inspired by this, generalized belief entropy is proposed. The belief entropy implies the relationship between Deng entropy, Rényi entropy, Tsallis entropy. In addition, numerical examples demonstrate the flexibility of the proposed Rényi-Deng (R-D) entropy to measure the uncertainty of basic probability assignment (BPA). Finally, a method for identifying contradictory evidence based on Rényi-Deng (R-D) entropy is proposed. The experiment show the effectiveness of the proposed method.
BackgroundAlthough artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made.ObjectiveWe aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient.MethodsAs the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material. All medical images were labeled to 4 types of diseases or normal (total 5 classes); each image was decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on different anatomical parts and foci observed in medical images, thereby bridging the gap between medical image and diagnostic process. Next, we applied images and the information produced during the annotation process to implement an interpretable and expandable automatic diagnostic framework with deep learning.ResultsThis diagnosis framework comprises 4 stages. The first stage identifies the type of disease (identification accuracy, 93%). The second stage localizes the anatomical parts and foci of the eye (localization accuracy: images under natural light without fluorescein sodium eye drops, 82%; images under cobalt blue light or natural light with fluorescein sodium eye drops, 90%). The third stage carefully classifies the specific condition of each anatomical part or focus with the result from the second stage (average accuracy for multiple classification problems, 79%-98%). The last stage provides treatment advice according to medical experience and artificial intelligence, which is merely involved with pterygium (accuracy, >95%). Based on this, we developed a telemedical system that can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between a doctor and a patient.ConclusionsThe interpretable and expandable medical artificial intelligence platform was successfully built; this system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information relevant to the diagnosis of diseases, and provide treatment suggestions. During this process, the whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctors. Owing to the rare high-grade medical resource, it is impossible that everyone receives high-quality professional diagnosis and treatment service. This framework can not only be applied in hospitals with insufficient medical resources to decrease the...
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