List Of Differential Diagnoses Research Articles

Wandering spleen presenting in the form of right sided pelvic mass and pain in a patient with AD-PCKD: a case report and review of the literature

BackgroundWandering spleen is a rare clinical entity in which the spleen is hypermobile and migrate from its normal left hypochondriac position to any other abdominal or pelvic position as a result of absent or abnormal laxity of the suspensory ligaments (Puranik in Gastroenterol Rep 5:241, 2015, Evangelos in Am J Case Rep. 21, 2020) which in turn is due to either congenital laxity or precipitated by trauma, pregnancy, or connective tissue disorder (Puranik in Gastroenterol Rep 5:241, 2015, Jawad in Cureus 15, 2023). It may be asymptomatic and accidentally discovered for imaging done for other reasons or cause symptoms as a result of torsion of its pedicle and infarction or compression on adjacent viscera on its new position. It needs to be surgically treated upon discovery either by splenopexy or splectomy based on whether the spleen is mobile or not.Case presentationWe present a case of 39 years old female Ethiopian patient who presented to us complaining constant lower abdominal pain especially on the right side associated with swelling of one year which got worse over the preceding few months of her presentation to our facility. She is primiparous with delivery by C/section and a known case of HIV infection on HAART. Physical examination revealed a right lower quadrant well defined, fairly mobile and slightly tender swelling. Hematologic investigations are unremarkable. Imaging with abdominopelvic U/S and CT-scan showed a predominantly cystic, hypo attenuating right sided pelvic mass with narrow elongated attachment to pancreatic tail and absent spleen in its normal position. CT also showed multiple different sized purely cystic lesions all over both kidneys and the pancreas compatible with AD polycystic kidney and pancreatic disease.With a diagnosis of wandering possibly infarcted spleen, she underwent laparotomy, the finding being a fully infarcted spleen located on the right half of the upper pelvis with twisted pedicle and dense adhesions to the adjacent distal ileum and colon. Release of adhesions and splenectomy was done. Her post-operative course was uneventful.ConclusionWandering spleen is a rare clinical condition that needs to be included in the list of differential diagnosis in patients presenting with lower abdominal and pelvic masses. As we have learnt from our case, a high index of suspicion is required to detect it early and intervene by doing splenopexy and thereby avoiding splenectomy and its related complications.

A Generative AI-Based Virtual Physician Assistant

We describe "Dr. A.I.", a virtual physician assistant that uses generative AI to conduct a pre-visit patient interview and to create a draft clinical note for the physician. We document the effectiveness of Dr. A.I. by measuring the concordance of the actual diagnosis made by the doctor with the generated differ-ential diagnosis (DDx) list. This application demonstrates the practical healthcare capabilities of a large language model to improve efficiency of doctor visits while also addressing safety concerns for the use of generative AI in the workflow of patient care.

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.

Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited. This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world. This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker's diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year). A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker. A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.

Computerized diagnostic decision support systems- a comparative performance study of Isabel Pro vs. ChatGPT4.

Validate the diagnostic accuracy of the Artificial Intelligence Large Language Model ChatGPT4 by comparing diagnosis lists produced by ChatGPT4 to Isabel Pro. This study used 201 cases, comparing ChatGPT4 to Isabel Pro. Systems inputs were identical. Mean Reciprocal Rank (MRR) compares the correct diagnosis's rank between systems. Isabel Pro ranks by the frequency with which the symptoms appear in the reference dataset. The mechanism ChatGPT4 uses to rank the diagnoses is unknown. A Wilcoxon Signed Rank Sum test failed to reject the null hypothesis. Both systems produced comprehensive differential diagnosis lists. Isabel Pro's list appears immediately upon submission, while ChatGPT4 takes several minutes. Isabel Pro produced 175 (87.1 %) correct diagnoses and ChatGPT4 165 (82.1 %). The MRR for ChatGPT4 was 0.428 (rank 2.31), and Isabel Pro was 0.389 (rank 2.57), an average rank of three for each. ChatGPT4 outperformed on Recall at Rank 1, 5, and 10, with Isabel Pro outperforming at 20, 30, and 40. The Wilcoxon Signed Rank Sum Test confirmed that the sample size was inadequate to conclude that the systems are equivalent. ChatGPT4 fabricated citations and DOIs, producing 145 correct references (87.9 %) but only 52 correct DOIs (31.5 %). This study validates the promise of Clinical Diagnostic Decision Support Systems, including the Large Language Model form of artificial intelligence (AI). Until the issue of hallucination of references and, perhaps diagnoses, is resolved in favor of absolute accuracy, clinicians will make cautious use of Large Language Model systems in diagnosis, if at all.

Embolic strokes of undetermined source: a clinical consensus statement of the ESC Council on Stroke, the European Association of Cardiovascular Imaging and the European Heart Rhythm Association of the ESC.

One in six ischaemic stroke patients has an embolic stroke of undetermined source (ESUS), defined as a stroke with unclear aetiology despite recommended diagnostic evaluation. The overall cardiovascular risk of ESUS is high and it is important to optimize strategies to prevent recurrent stroke and other cardiovascular events. The aim of clinicians when confronted with a patient not only with ESUS but also with any other medical condition of unclear aetiology is to identify the actual cause amongst a list of potential differential diagnoses, in order to optimize secondary prevention. However, specifically in ESUS, this may be challenging as multiple potential thromboembolic sources frequently coexist. Also, it can be delusively reassuring because despite the implementation of specific treatments for the individual pathology presumed to be the actual thromboembolic source, patients can still be vulnerable to stroke and other cardiovascular events caused by other pathologies already identified during the index diagnostic evaluation but whose thromboembolic potential was underestimated. Therefore, rather than trying to presume which particular mechanism is the actual embolic source in an ESUS patient, it is important to assess the overall thromboembolic risk of the patient through synthesis of the individual risks linked to all pathologies present, regardless if presumed causally associated or not. In this paper, a multi-disciplinary panel of clinicians/researchers from various backgrounds of expertise and specialties (cardiology, internal medicine, neurology, radiology and vascular surgery) proposes a comprehensive multi-dimensional assessment of the overall thromboembolic risk in ESUS patients through the composition of individual risks associated with all prevalent pathologies.

Assessing ChatGPT 4.0’s test performance and clinical diagnostic accuracy on USMLE STEP 2 CK and clinical case reports

While there is data assessing the test performance of artificial intelligence (AI) chatbots, including the Generative Pre-trained Transformer 4.0 (GPT 4) chatbot (ChatGPT 4.0), there is scarce data on its diagnostic accuracy of clinical cases. We assessed the large language model (LLM), ChatGPT 4.0, on its ability to answer questions from the United States Medical Licensing Exam (USMLE) Step 2, as well as its ability to generate a differential diagnosis based on corresponding clinical vignettes from published case reports. A total of 109 Step 2 Clinical Knowledge (CK) practice questions were inputted into both ChatGPT 3.5 and ChatGPT 4.0, asking ChatGPT to pick the correct answer. Compared to its previous version, ChatGPT 3.5, we found improved accuracy of ChatGPT 4.0 when answering these questions, from 47.7 to 87.2% (p = 0.035) respectively. Utilizing the topics tested on Step 2 CK questions, we additionally found 63 corresponding published case report vignettes and asked ChatGPT 4.0 to come up with its top three differential diagnosis. ChatGPT 4.0 accurately created a shortlist of differential diagnoses in 74.6% of the 63 case reports (74.6%). We analyzed ChatGPT 4.0’s confidence in its diagnosis by asking it to rank its top three differentials from most to least likely. Out of the 47 correct diagnoses, 33 were the first (70.2%) on the differential diagnosis list, 11 were second (23.4%), and three were third (6.4%). Our study shows the continued iterative improvement in ChatGPT’s ability to answer standardized USMLE questions accurately and provides insights into ChatGPT’s clinical diagnostic accuracy.

Evaluating ChatGPT-4's Diagnostic Accuracy: Impact of Visual Data Integration.

In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. This study aims to assess the impact of adding image data on ChatGPT-4's diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data. We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis. The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V's performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4's self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases. Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.

Red in the face: Approach to diagnosis of red rashes on the face.

A red rash on the face in an adult patient is a common presentation to general practice in Australia. Rashes on the face significantly affect quality of life because this is a cosmetically sensitive site. Ascertaining the correct diagnosis is therefore of utmost importance so that appropriate treatment can be initiated. This article discusses the assessment of red rashes on the face in an adult patient. Diagnosing a red rash on the face requires assessment of symptomology, age of onset, rash morphology and 'clinical clues' that help delineate between differentials. Although the list of differential diagnoses is wide, many of the common diagnoses can be made clinically without the need for investigations. Investigations such as skin biopsy are useful if the diagnosis is unclear, if the rash is not responding to initial treatment and/or a referral to a dermatologist is being considered.

Diagnosis and treatment of epiploic appendagitis in a Middle Eastern country: An observational retrospective analysis of 156 cases.

Epiploic appendagitis (EPA) is an uncommon emergency surgical condition that causes acute abdominal pain, rendering a list of differential diagnoses. Therefore, careful examination and imaging tools are required. EPA is a self-limiting condition that can be resolved in 1-2weeks and rarely needs surgical intervention. Its low incidence makes EPA less well-known among the public and some medical professionals, and it is frequently under-diagnosed. We aimed to explore the incidence, clinical presentation, modalities of imaging to diagnose and options for treatingEPA. An observational retrospective analysis was conducted between 2016 and 2022 at a tertiary hospital in an Arab Middle Eastern country. There were 156 EPA cases diagnosed over six years, with a mean age of 33years. Males represented 82% of the cohort. The entire cohort was treated non-operatively except for eight patients who had surgical intervention using open or laparoscopic surgery. The diagnosis was made by a computerized tomographic scan (CT). However, plain X-ray, abdominal ultrasound, and magnetic resonance imaging (MRI) were performed initially in a few selected cases to rule out other conditions. No specific blood test indicated EPA; however, a histopathology examination was diagnostic. No mortality was reported in the study cohort. This is the most extensive study analyzing EPA patients from the Middle East. EPA is a rare and mostly self-limiting acute abdominal disorder; however, early ultrasound and CT scan can pick it up quickly after a high index of suspicion.

Can ChatGPT-4 evaluate whether a differential diagnosis list contains the correct diagnosis as accurately as a physician?

The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals' assessments. We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient. Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement. ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists.

Evaluation of remote assistance for point-of-care ultrasonography in a large animal hospital: a controlled randomized trial.

To assess whether remote assistance is beneficial to the accuracy of point-of-care ultrasound (POCUS) examinations during equine emergency evaluations. Adult horses presenting to a tertiary care large animal emergency service for respiratory or gastrointestinal clinical signs. Prospective, randomized, controlled, clinical study. Cases were assigned to receive POCUS with remote assistance ("POCUS-R" group) or POCUS with no assistance ("POCUS" group). Accuracy of diagnosis, duration of sonograms and survival to discharge were the objective outcomes compared between groups. Perceptions about the use of teleultrasound were gathered using a survey. 29 cases were enrolled in the study over an 18-month period. Based on Cox regression estimates, sonogram duration was likely to be longer in the POCUS-R group (n = 13) relative to the POCUS group (16) hazard ratio 0.43 (95% CI, 0.9 to 0.2; P = .03). Whether the final diagnosis was included in the differential diagnosis list, number of diagnoses in the differential list and survival were not different between groups. Educational benefits were the most frequently mentioned benefit by clinician users while logistical and technical problems related to the use of the equipment and inconvenience of enrollment in the study was the most common negative aspect. Teleultrasound was generally not well adopted by users likely due to inconvenience and time constraints. Further work is indicated to assess differences in clinical outcomes in a setting that would allow a larger sample size to increase statistical power.

DRESS syndrome: More than just a rash.

Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is rare but carries significant mortality and morbidity, making early identification and definitive management crucial. The diagnosis of DRESS is made clinically and involves consideration of a broad list of differential diagnoses. Given variable clinical presentations among patients with DRESS syndrome, clinicians should look for common findings and other hallmarks of the syndrome while monitoring for known complications. Additionally, clinicians should maintain a high index of suspicion to avoid missing more mild presentations, such as in this case patient with DRESS syndrome minor.

FRAMED: a framework facilitating insight problem solving.

Insight has been studied as an element of problem solving in the field of cognitive psychology and may play an important role in clinical reasoning. We propose a new strategy based on theories that promote insight that may help generate further diagnostic hypotheses by reviewing the interpretation of a case and an individual's list of differential diagnoses from multiple perspectives: formation (F), re-encoding (R), analogy (A), modification (M), elaboration (E), and deliberation (D) (FRAMED). The FRAMED strategy may help clinicians overcome misinterpretations and cognitive bias by systematically reflecting on previous clinical reasoning processes from multiple perspectives.

A rare case of caseous necrosis of mitral valve

Caseous calcification of the mitral annulus, an uncommon kind of intracardiac mass, is a form of degenerative disease that afflicts the mitral valve causing liquefactive or caseous necrosis. Caseous calcification of mitral annulus is rarely encountered in practice; however, it should be on the list of differential diagnosis of any intracardiac masses located close to the mitral annulus.

Diagnostic challenge in a 2‐year‐old boy poisoned with carbon monoxide: A case report

Key Clinical MessageCarbon monoxide poisoning diagnosis is sometimes very difficult and should be considered in the differential diagnosis list of children's consciousness disorders even in summer.AbstractCarbon monoxide poisoning is very dangerous, and sometimes, it is difficult to diagnose. Especially, this poisoning may have non‐specific manifestations in children and can be confused with other diseases. Here we present a 2‐year‐old child who suffered a disturbance of consciousness during the summer season. He and his family were travelers who had come to Mashhad from Kerman and were staying in a hotel room, after various investigations. It has been found that he was poisoned with carbon monoxide gas due to the leakage of carbon monoxide gas from the chimney pipe of the engine room related to the hotel's water heating into the room. After receiving oxygen and supportive treatments, he recovered and was discharged with good health. Poisoning with carbon monoxide gas is very dangerous, and the awareness of health and medical personnel in this field is essential.

Demodicosis in a Kunekune pig and molecular characterisation of porcine demodectic mites involved: a case report

BackgroundIn January 2021, a female 1-year-old Kunekune was presented at the University Clinic for Swine with severe reduction of the field of vision resulting in prolonged reaction time when targeting barriers, due to moderate to severe thickening of the skin around both orbits also affecting the eyelids. MethodsClinical examination revealed skin hyperplasia, nodular enlargement of the skin pores of the axillar and inguinal region. Ophthalmologists decided to remove parts of the thickened periocular skin, followed by histopathological examination.ResultsOnce large amounts of demodectic mites were detected by histopathology, demodicosis could be diagnosed and treatment of the pig was started using sarolaner. Morphological and molecular analyses were performed. Histopathological and parasitological exams led to the aetiological diagnosis of demodicosis in the affected Kunekune pig. Severe skin lesions were revealed to be the consequence of an infestation with Demodex sp. Morphological analyses confirmed the involvement of D. phylloides. Molecular characterization indicated a Demodex species closely related to mites documented in wild boar - most probably D. phylloides for which no explicit sequences are available in GenBank yet. Treatment with sarolaner (2.6 mg/kg) resulted in a substantial regression of skin lesions, already detectable 1 month after first treatment.ConclusionsDemodicosis is a very rare disease in pigs that is most probably related to an impaired immune response to the mites. Demodectic mange should be included in the list of differential diagnoses in cases of periocular alterations of the skin of pigs.Graphical

Gynaecological Artificial Intelligence Diagnostics (GAID) GAID and Its Performance as a Tool for the Specialist Doctor.

Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is such a system. Based on artificial intelligence (AI) argumentation technology, it was developed to incorporate, as much as possible, a complete representation of the medical knowledge in gynaecology and to become a real-life tool that will practically enhance the quality of healthcare services and reduce stress for the clinician. Our study aimed to evaluate GAIDS' efficacy and accuracy in assisting the working expert gynaecologist during day-to-day clinical practice. Knowledge-based systems utilize a knowledge base (theory) which holds evidence-based rules ("IF-THEN" statements) that are used to prove whether a conclusion (such as a disease, medication or treatment) is possible or not, given a set of input data. This approach uses argumentation frameworks, where rules act as claims that support a specific decision (arguments) and argue for its dominance over others. The result is a set of admissible arguments which support the final decision and explain its cause. Based on seven different subcategories of gynaecological presentations-bleeding, endocrinology, cancer, pelvic pain, urogynaecology, sexually transmitted infections and vulva pathology in fifty patients-GAID demonstrates an average overall closeness accuracy of zero point eighty-seven. Since the system provides explanations for supporting a diagnosis against other possible diseases, this evaluation process further allowed for a learning process of modular improvement in the system of the diagnostic discrepancies between the system and the specialist. GAID successfully demonstrates an average accuracy of zero point eighty-seven when measuring the closeness of the system's diagnosis to that of the senior consultant. The system further provides meaningful and helpful explanations for its diagnoses that can help clinicians to develop an increasing level of trust towards the system. It also provides a practical database, which can be used as a structured history-taking assistant and a friendly, patient record-keeper, while improving precision by providing a full list of differential diagnoses. Importantly, the design and implementation of the system facilitates its continuous development with a set methodology that allows minimal revision of the system in the face of new information. Further large-scale studies are required to evaluate GAID more thoroughly and to identify its limiting boundaries.

ChatGPT and Beyond: An overview of the growing field of large language models and their use in ophthalmology.

ChatGPT, an artificial intelligence (AI) chatbot built on large language models (LLMs), has rapidly gained popularity. The benefits and limitations of this transformative technology have been discussed across various fields, including medicine. The widespread availability of ChatGPT has enabled clinicians to study how these tools could be used for a variety of tasks such as generating differential diagnosis lists, organizing patient notes, and synthesizing literature for scientific research. LLMs have shown promising capabilities in ophthalmology by performing well on the Ophthalmic Knowledge Assessment Program, providing fairly accurate responses to questions about retinal diseases, and in generating differential diagnoses list. There are current limitations to this technology, including the propensity of LLMs to "hallucinate", or confidently generate false information; their potential role in perpetuating biases in medicine; and the challenges in incorporating LLMs into research without allowing "AI-plagiarism" or publication of false information. In this paper, we provide a balanced overview of what LLMs are and introduce some of the LLMs that have been generated in the past few years. We discuss recent literature evaluating the role of these language models in medicine with a focus on ChatGPT. The field of AI is fast-paced, and new applications based on LLMs are being generated rapidly; therefore, it is important for ophthalmologists to be aware of how this technology works and how it may impact patient care. Here, we discuss the benefits, limitations, and future advancements of LLMs in patient care and research.

External Auditory Canal Granulations of Varying Aetiology Other than Cholesteatoma: A Series of Seven Cases

External Auditory Canal (EAC) granulations can occur due to various causes, each with different presenting features, treatment options, and prognoses. They can arise from trivial situations, such as a neglected Foreign Body (FB), or indicate serious pathology like squamous cell carcinoma. The most common causes of EAC granulations are otitis media, including squamous and mucosal types. However, clinical practice encounters numerous other rare and common causes. The present case series describes seven patients with EAC granulations resulting from different aetiologies apart from Chronic Suppurative Otitis Media (CSOM). The study was conducted over a six-month period at a Government Medical College (GMC) in North Odisha. It aims to assist clinicians in considering these situations when managing patients with EAC granulation. Proper diagnosis and treatment of these patients resulted in successful outcomes. Therefore, the author recommends considering these underlying causes whenever EAC granulations do not respond to appropriate medical treatment with antibiotics. Radiological imaging, fine needle aspiration cytology, and histopathological studies can help exclude many of these causes from the list of differential diagnoses.

Epistaxis and Facial Swelling Due to Nasal Blastomycosis in a Cat.

A 5 yr old castrated male domestic longhair was examined because of left-sided facial swelling and epistaxis. Head computed tomography with contrast identified a mass within the left nasal cavity and multifocal regions of nasal bone osteolysis. Histopathology of nasal mass biopsies and cytology of the facial swelling revealed pyogranulomatous inflammation due to Blastomyces dermatitidis. The cat experienced resolution of clinical signs following 8 mo of treatment with itraconazole. Although rare, clinicians should include blastomycosis on the differential diagnoses list of infectious causes for feline nasal disease if within an endemic area.