OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.
METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher\'s exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.
RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).
CONCLUSIONS: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

This review is intended to familiarize readers with an emerging group of fungal infections that mostly manifest in immunocompetent individuals. This group was initially considered endemic to the tropics, but increasing worldwide prevalence has been reported. The organisms have been divided into dominant non-invasive forms and dominant invasive forms for ease of understanding. The non-invasive organisms include the group Entomophthoromycota, under which two genera Basidiobolus and Conidiobolus, have been identified as human pathogens. They present with plaques in the extremities and rhinofacial region, respectively. The invasive organisms are dematiaceous fungi (phaeohypomycosis), which includes Cladophialophora and Exophiala among others. They cause invasion of deep tissues, with the central nervous system being the most common target. The mycology, epidemiology, diagnosis, and treatment options have been summarized in brief. The clinical presentation, imaging manifestations, differentiation from other common infections and malignancies that show similar features have been detailed.

UNASSIGNED: Postmortem computed tomography (pmCT) prior to forensic autopsy has become increasingly important in recent decades, especially in forensic documentation of single injuries, injury patterns, and causes of death. Postmortem decomposition gas formation can also be detected in pmCT scans, which might affect cochlear implant research in postmortem human temporal bones (TBs).
UNASSIGNED: Fifty non-putrefied hanging fatalities within a 2-year period (January 2017 to December 2019) were included with 100 TBs. Each body underwent whole-body pmCT prior to forensic autopsy. PmCT scans were analyzed with respect to the presence of intracochlear gas despite the lack of putrefaction at autopsy by an experienced fellow neurotologist.
UNASSIGNED: PmCT revealed gas formation in two individuals despite the lack of head trauma and putrefaction at postmortem examination and autopsy. Both individuals showed enclosed gas in the vestibule and the cochlea on both sides.
UNASSIGNED: Intracochlear gas formation, most likely related to decomposition, may occur despite the lack of putrefaction at postmortem examination and autopsy and can be detected by pmCT. This finding seems to be rather rare in non-traumatic death cases but might affect cochlear pressure research in postmortem human TB.

UNASSIGNED: This prospective observational study aimed to explore the diversity in lacrimal pathway morphology among patients with congenital nasolacrimal duct obstruction (CNLDO) by examining dacryocystography (DCG) images.
UNASSIGNED: The study included 64 patients who underwent DCG before undergoing general anesthesia probing for unilateral CNLDO. Several parameters were measured from the lateral view of the DCG images: (1) the lacrimal sac (LS) and the nasolacrimal duct (NLD) angle, (2) the angle formed by the superior orbital rim (SOR), LS, and the NLD, (3) LS length, and (4) bony NLD length. Additionally, frontal views of the DCG images were utilized to measure (5) LS-NLD angle and (6) LS angle concerning the midline.
UNASSIGNED: The average age of the patients was 34.3 months. The mean ± standard deviation of the measurements of the above parameters was (1) -1.2° ± 16.5° (range: -44.6° ± 46.6°), (2) -5.0° ± 10.3° (range: -24.0° ± 19.0°), (3) 10.2 ± 2.4 mm (range: 6.5-16.0 mm), (4) 8.0 ± 2.5 mm (range: 3.1-14.8 mm), (5) 15.6° ± 11.2° (range: -16.8° ± 41.0°), and (6) 15.1 ± 5.2° (range: 3.3°-29.8°). All parameters, except for parameter (3), conformed to a normal distribution.
UNASSIGNED: This study provides valuable anthropometric data derived from DCG images, highlighting the substantial variability in lacrimal pathway morphology among patients with CNLDO. Furthermore, anatomical constraints made probing with a straight metal bougie anatomically infeasible in 25.0% of the patients included in this study.
Understanding the morphology of the lacrimal pathway is crucial for the successful probing treatments in patients with congenital nasolacrimal duct obstruction (CNLDO). This study represents an initial effort to quantify anthropometric parameters of the lacrimal drainage system through dacryocystography images, specifically aiming to highlight the limitations of blind probing procedure. The results underscore significant variations in the morphology of the lacrimal drainage system among patients, which could impact diagnostic approaches and treatment strategies. Additionally, the findings suggest that patients with CNLDO who do not respond to blind probing may have underlying anatomical complexities. Therefore, rather than relying on repeated blind probing, employing dacryoendoscopy-guided probing under direct visualization could offer a more effective therapeutic alternative for complicated cases of CNLDO.

UNASSIGNED: Omental infarction (OI) is an uncommon cause of acute abdominal pain. A high index of clinical suspicion is required for diagnosis of OI as the incidence is less than 1 %, presenting with abdominal pain. We report primary OI\'s clinical and radiological profile from a single tertiary care hospital in India.
UNASSIGNED: In this retrospective cross-sectional study, the electronic medical and radiology records of patients with abdominal pain were reviewed over seven years (2015-2022). Variables were systematically collected and analyzed.
UNASSIGNED: A total of 22 patients diagnosed with primary OI were included in this study. Male preponderance (63.6 %) was noted with a mean age of 47.45 years (SD ± 13.84; range: 18-72 years). Most patients belonged to class I obesity (according to the Asia-Pacific body mass index classification) with a mean BMI of 26.56 kg/m2 (SD ± 3.21 kg/m2). All patients had abdominal pain as the primary symptom, with a mean duration of 8.64 days (SD ± 10.15; range: 1-42 days). The most common locations of pain were the right hypochondrium (27.3 %) and diffuse (27.3 %), followed by the right iliac fossa (18.1 %). Most (95.45 %, n=21/22) patients were treated conservatively, and only one required surgical intervention.
UNASSIGNED: Primary OI is a rare and benign cause of acute abdomen. Obesity is a risk factor but does not correlate with the size or severity of OI. Radiological imaging, like a computed tomography (CT) scan, is essential for diagnosis. A conservative management line should be the first approach in treating primary OI before considering surgical options.

OBJECTIVE: Radiology faculty across various specialties have been reported to receive an average of 20.7 invitations to submit manuscripts to bogus journals and 4.1 invitations to speak at unsuitable events over a two-week span. Radiology trainees also receive a fair number of unsolicited invitations from unknown senders to submit manuscripts and speak at meetings. Trainees can be more vulnerable to predatory invitations due to potential naivety. We aimed to determine the prevalence of these spam invitations received by radiology trainees.
METHODS: The designed survey for evaluating the experience of radiology trainees regarding phishing scams of predatory publications and conferences was sent to radiology residency and neuroradiology fellowship program leadership to redistribute amongst their trainees, and was advertised on social media platforms. The survey was first sent out on September 28, 2023, and was closed two weeks later October 12, 2023. Spearman\'s correlation, univariable and multivariable linear regression analyses were performed.
RESULTS: Our study included 151 respondents who completed the survey. Of the survey respondents, 53 % reported receiving unsolicited emails from predatory publications (mean = 6.76 ± 7.29), and 32 % reported receiving emails from fraudulent conferences (mean = 5.61 ± 5.77). Significant positive correlation was observed between number of unsolicited email invitations with number of PubMed indexed publications, number as corresponding author, number in open access journals and number of abstract presentations.
CONCLUSIONS: Trainees in radiology receive many unsolicited invitations to publish papers as well as to present at meetings that are not accredited. This could lead to wasted time and financial resources for unsuspecting trainees.

OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).
METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.
RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.
CONCLUSIONS: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

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