BMC Med Inform Decis Mak - Towards case-based medical learning in radiological decision making using content-based image retrieval.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ system(1976) rule(880) can(841) }
{ case(1353) use(1143) diagnosi(1136) }
{ framework(1458) process(801) describ(734) }
{ can(774) often(719) complex(702) }
{ ehr(2073) health(1662) electron(1139) }
{ use(1733) differ(960) four(931) }
{ imag(1947) propos(1133) code(1026) }
{ search(2224) databas(1162) retriev(909) }
{ system(1050) medic(1026) inform(1018) }
{ assess(1506) score(1403) qualiti(1306) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ record(1888) medic(1808) patient(1693) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ decis(3086) make(1611) patient(1517) }
{ sequenc(1873) structur(1644) protein(1328) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ import(1318) role(1303) understand(862) }
{ implement(1333) system(1263) develop(1122) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ studi(1410) differ(1259) use(1210) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ inform(2794) health(2639) internet(1427) }
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{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
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{ featur(1941) imag(1645) propos(1176) }
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{ health(3367) inform(1360) care(1135) }
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{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education.METHODS: We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment.RESULTS: We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system.CONCLUSIONS: The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.

Resumo Limpo

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