Two cases of Taeniasis Infection.

We report two cases of taeniasis caused by tapeworm infection. The first was a Japanese female, 23 years old, who had a history of eating raw meat during a visit to Thailand. She was referred to our hospital with a history of passing proglottids in feces. Taenia saginata or T. asiatica was suspected based on the proglottid morphologic features in addition to supportive information regarding her travel and dietary history. The patient was given praziquantel and the tapeworm was excreted. The second was a 35-year-old Thai male who had lived in Japan since 2000 and not left the country since that time. He had consumed beef cooked in the so-called yakiniku style and also sometimes raw, because of nostalgia for that Thai custom. The patient passed proglottids several times and then came to us. The proglottids were compatible with those of T. saginata. Praziquantel was prescribed and the tapeworm was excreted. In both cases, mitochondrial DNA analysis identified the worm species as T. saginata. Since morphological discrimination of three human-infecting Taenia species, T. saginata, T. solium, and T. asiatica, is not always possible, it is necessary to employ DNA analysis for diagnosis of taeniasis to confirm the worm species

Creating DICOM Structured Reporting Object from Free Text Reports Using the Text Mining Method

Purpose:DICOM Structured Reporting has advantages, however, many existing reporting systems create and store in free-text format. In this study, we developed and evaluated the automated method for creating structured reports from free-text format. Methods:100 NM reports (internal data-set)of brain perfusion scintigraphy were analyzed and categorized into expression units using text-mining technology to create a dictionary. Using this dictionary, other 100 reports (external data-set)were analyzed for the accuracy. The results were also compared against manual categorization by physicians. Results:In internal data-set, 79.5% of sentences has matched, and in external data-set 62.2% of sentences has matched to convert into units.Discussion:Matching rate can be increased by improving the dictionary. In addition, byautomatically creating DICOM structured reports, clinical information can be easily translated into other languages, transferred to other systems, and can be searched rapidly.RSNA\u2705 91th Scientific Assembly and Annual Meetin

Impact of enhanced digital watermark in medical images - Improvement of the security by using demographic watermarked patient-ID and institution-ID

By using embedding watermark into medical images, we can secure the protection of mishandling and the originality of image. We used two types of watermark methods, we embedded complex watermark. We can detect the falsification by the sensitive watermark for modification of image. On the other hand, we can maintain the patient identification information by the resistant watermark to image processing and we can know the demographic information about the institution and the patient. We developed the embedding method of watermark and the error correction method of extracted data and also can keep the image deterioration in low level. If the extracted information has 30% error data, we can repair the information completely by combined error correction method. There is a dilemma between the watermark tolerance to image processing and deterioration in image quality. We decided the suitable trade-off point and we will show the watermarked images and the new embedding/error-correction methods.RSNA\u2704 90th Scientific Assembly and Annual Meetin

Zagros Spice Mills: the Simurrean and the Hašimur grindstones

Background:There is a trend towards digitization of reporting systems, which are mainly used in the research and creation of diagnostic reports. However, the current situation is not at a satisfactory level in terms of effective use of digital information. The main reason is considered to be related to diagnostic reports, which are generally in free text format, thus making it difficult to utilize the descriptive contents systematically. \nPurpose:We have developed a method for automatically creating structured reports in free text format. This method uses a text mining tool that extracts semantic information from the contents in free text format to enable semantic interpretation of the information. \nResult:The match rate is 70.2% when the number of learning reports is 100, it rises to 85.5% when the number of learning reports is increased to 500. In addition, when the number of learning reports is about 300 or more, the match rate tends to saturate and becomes nearly constant. At the same, however, this demonstrates that there is a certain mismatch rate remaining. The unexpected expression unit rate is about 15% when the number of learning reports is small, it drops to 6.8% when the number of learning reports increases to 500. This indicates that increasing the number of learning reports tends to reduce the number of unexpected expression units.\nConclusion:Structuring of diagnostic reports in free text format was performed using the text mining tool in order to make the contents available for secondary uses. As a result of comparison of the converted expression units with those manually generated by physicians, a match rate of 85.8% was achieved.A method for translating the contents of reports from Japanese to English using structured reports in a simple manner was also presented.EuroPACS　200

Conversion of Japanese free-text radiology Reports to DICOM structured reports using translated RadLex terminology.

-Background-DICOM Structuring Reporting (DICOM-SR) complying with RadLex terminology has more advantage of improving retrieval performance of radiology reports. The aim of this study was to create DICOM-SR object with RadLex Terminology from Free-Text Radiology Reports.-Evaluation-In RSNA 2007, we had proposed the method that can structuralize free-text radiology reports written in Japanese with enough precision. The method consisted of two algorithms: the first one was to extract words from free-text radiology reports with reference to the adjusted dictionary, and the second one was to create the semantic combination of extracted words to describe contents of radiology reports.In this study, we developed the system to automatically create a DICOM-SR object by correlating RadLex terms with words extracted from radiology reports.We randomly selected 300 Japanese words which were extracted from radiology reports of thoracic CT and cerebral perfusion scintigraphy, and assessed the matching ratio between the selected words and RadLex terms of the secondary version. As a result, the matching ratio was 74%.-Discussion-Some terms are undefined in RadLex of the secondary version, e.g., terms categorized into \u27Visual feature\u27 and into \u27Visual or Casual Relationships\u27 which define relationship between findings described in free-text radiology reports using adjective phrases and narrative expression. We considered it necessary for improvement of matching ratio that RadLex terminology is continuously extended based on analyzingcontents of radiology reports. With the RadLex Relationship hierarchy between combinations of terms, we expect to construct the advanced search mechanism of radiology reports.-Conclusion-DICOM-SR object with the combination of RadLex terms was automatically generated from free-text radiology reports by our developed system with the structuring method.RSNA\u2708 94th Scientific Assembly and Annual Meetin

The Structuring Method of Diagnostic Reports by Using Description Units for Semantic Interpretation

PURPOSEIt is useful for references and making diagnosis to search similar case using diagnostic reports from past diagnostic reports that was electronically stored. However, it is difficult to systematically and semantically interpret the descriptive contents of the reports which are generally expressed using free-text format. At first, we have constructed the conversion method from free text format to semantic structure and next the system to create structured reports with semantic structure \u27description unit\u27 that we defined from free-text reports. In this study, we proposed the reference system to search similar diagnostic reports using semantic sentence.\nMETHOD and MATERIALSStructured diagnostic reports were converted into the description units which were classified into two types of finding units and diagnostic units. The finding and diagnostic units included finding/diagnosis, modifier, region, regional modifier, and confidence. Diagnostic reports in free-text format were analyzed and classified into the description units by using a text mining method. To search similar case, we developed the retrieval system with the following algorithms: (1) to convert description unites from findings or impressions that physicians wrote, (2) to find past stored reports according the higher match rate. The match rate was defined as the identical ratio of number of description units extracted from findings or impressions, to number of it from past reports. In this study, we assessed the usefulness of thissystem by applying to clinical diagnostic reports of chest CT studies.\nRESULTSThe accuracy of our conversion method was 90% at recall rate. The system could find over 60% of the similar reports that physician requested from 1,500 cases of past reports.\nCONCLUSIONOur newly developed system could search similar cases using semantic analysis. In addition, it was expected to search similar images by free text findings.RSNA\u2707 93th Scientific Assembly and Annual Meetin